The external aggregate travel models predict characteristics of US-SD and SD-US/MX travel behavior for all non-commercial, non-visitor vehicle trips and selected transit trips. Note that non-commercial MX-SD trips are forecast in the crossborder model, and non-commercial SD-US and SD-MX trips are forecast in the resident model.
+
+
External Model Estimation of Trip Counts by Type ¶
+
The total count of trips by production and attraction location was estimated in a series of steps:
+
+
The number of crossborder trips made by Mexican residents to attractions in San Diego was previously determined.
+
The trips in the resident travel survey were expanded to estimate the total number of trips made by San Diego residents to attractions in Mexico.
+
To derive an estimate of the number of US-MX trips, the number of MX-SD (1) and SD-MX (2) trips was subtracted from the total number of border-crossings.
+
The distribution of US-MX trips among external stations on the US-side of San Diego County will be assumed to be proportional to the total volume at each external station, regardless of the point of entry at the Mexican border.
+
+
+
The number of US-MX trips was then subtracted from the total number of trips in the SCAG cordon survey to arrive at an estimate of the combined total of US-US, US-SD, and SD-US trips with routes through San Diego County.
+
Finally, the actual amounts of US-US, US-SD, and SD-US trips at each external station were estimated from the remaining trips (4) according to their proportions in the successfully geocoded responses in the SCAG cordon survey.
The behavioral characteristics of the different types of external trip were derived from the various data sources available as follows:
+
+
US-US trips: a fixed external station OD trip matrix was estimated from the SCAG cordon survey.
+
/src/main/emme/toolbox/model/external_external.py
+
+
+
US-MX trips: a fixed external station OD trip matrix was estimated from the SCAG cordon survey, Customs and Border Protection vehicle counts, and Mexican resident border-crossing survey as described in the previous section.
+
/src/main/emme/toolbox/model/external_external.py
+
+
+
US-SD trips: rates of vehicle trips per household for each external county were developed from the SCAG cordon survey, and the trips were distributed to locations in San Diego County according to a destination choice model estimated from the interregional survey.
+
Intermediate stops and transit trips are not modeled in this segment due to the small contribution of these events to the total demand in the segment.
The external-internal destination choice model distributes the EI trips to destinations within San Diego County. The EI destination choice model explanatory variables are:
+
+
Distance
+
The size of each sampled MGRA
+
+
Diurnal and vehicle occupancy factors are then applied to the total daily trip tables to distribute the trips among shared ride modes and different times of day.
The trips are then split among toll and non-toll paths according to a simplified toll choice model. The toll choice model included the following explanatory variables:
This website describes the travel demand modeling system developed by the San Diego Association of Governments (SANDAG). SANDAG plans for many complex mobility issues facing the San Diego region, including development of the Regional Plan. Transportation models are complex analysis tools used to provide transportation planners and policymakers with information to help allocate scarce resources fairly and equitably. As we plan for the future, models are used to forecast potential future scenarios of where people will live and how they will travel. They are the principal tool used for alternatives analysis.
The SANDAG transportation model is an activity-based model (ABM). It simulates individual and household transportation decisions that make up their daily travel. This includes all trips people make on a daily basis, such as to work, school, shopping, healthcare, and recreation. An ABM provides a controlled, analytical platform so that different inputs and alternatives can be evaluated to predict whether, when, and how this travel occurs. SANDAG ABM accounts for a variety of different weekday travel markets in the region, including San Diego region resident travel, travel by Mexico residents and other travelers crossing San Diego County\u2019s borders, visitor travel, airport passengers at both the San Diego International Airport and the Cross Border Xpress bridge to the Tijuana International Airport, and commercial travel.
The most recent version of the SANDAG ABM is referred to as \u201cABM3\u201d, and was developed for use in the 2025 Regional Plan. ABM3 is a significant enhancement from ABM2+ which was used for the 2021 Regional Plan. All of the passenger demand models in ABM2+ were converted from CT-RAMP to ActivitySim, including resident travel, cross-border travel, visitor travel, and airport travel. The internal-external travel component is now fully integrated with the resident model. The model was also enhanced to improve the representation of household and person-based mobility, vehicle fleet ownership, transit, shared and private micro-mobility, and micro-transit. Many of the model components were re-estimated using household survey data collected in 2022, and all model components were re-calibrated to base-year 2023 conditions. A new disaggregate commercial vehicle model was developed based upon a 2020 commercial vehicle survey, and implemented in ActivitySim.
This website includes a description of the model system, how to set up and run the models, and a description of model inputs and outputs. Some aspects of the site are a work-in-progress, so we recommend that you check in often, and share your thoughts on ways to improve the site with SANDAG Transportation Modeling staff. Thank you!
"},{"location":"cvm.html","title":"Commercial Vehicle Model","text":""},{"location":"cvm.html#design","title":"Design","text":""},{"location":"cvm.html#inputs","title":"Inputs","text":""},{"location":"cvm.html#outputs","title":"Outputs","text":""},{"location":"faq.html","title":"FAQs","text":""},{"location":"faq.html#how-many-abm-versions-does-sandag-maintain","title":"How many ABM versions does SANDAG maintain?","text":"
There are four released ABM versions - ABM1, ABM2, ABM2+, and ABM3.
"},{"location":"faq.html#who-uses-the-sandag-abm-and-what-for","title":"Who uses the SANDAG ABM and what for?","text":"
The SANDAG ABM is used by SANDAG and many other public and private entities in the San Diego region. These entities include the City of San Diego and other local jurisdictions, Caltrans District 11, San Diego Metropolitan Transit System, North County Transit District, and private developers. Typical ABM applications include analysis for regional planning, air quality conformity, corridor studies, and land use development impact studies.
"},{"location":"faq.html#what-model-components-does-the-sandag-abm-have","title":"What model components does the SANDAG ABM have?","text":"
The SANDAG ABM is a suite of models covering various travel demand markets in the San Diego region. The microsimulation model components include a San Diego resident model, a commercial vehicle model, a Mexican resident crossborder model, a visitor model, a San Diego International Airport ground access model, a Cross-Border Express model serving Tijuana International Airport, and a special event model. The aggregate model components include an external heavy truck model and external trip models.
"},{"location":"faq.html#what-is-the-base-year-of-the-sandag-abm","title":"What is the base year of the SANDAG ABM?","text":"
ABM2 has a base year of 2022. ABM1 base year was 2012 and both ABM2 and ABM2+ have a base year of 2016.
"},{"location":"inputs.html","title":"ABM3 Model Inputs","text":"
The main inputs to ABM3 include the transportation network, land-use data, synthetic population data, parameters files, and model specifications. Outputs include a set of files that describe travel decisions made by all travel markets considered by the model (residents, overnight visitors, airport ground access trips, commercial vehicles and trucks, Mexico residents traveling in San Diego County, and travel made by all other non-residents into and through San Diego County).
There are several file types used for model inputs and outputs. They are denoted by their extension, as listed in the table below.
Extension Format .log, .txt Text files created during a model run containing logging results. .yaml Text files used for setting properties that control ActivitySim or some other process. .csv Comma-separated value files used to store model parameters, input or output data. .omx Open matrix format files used to store input or output trip tables or skims .h5 HDF5 files, used to store pipeline for restarting ActivitySim .shp (along with other files - .cpg, .dbf, .prj, .shx) ArcGIS shapefiles and associated files .html Hypertext markup language files, open in web browser .png Portable network graphics file, open in web browser, Microsoft photos, or third-party graphics editor"},{"location":"inputs.html#model-inputs","title":"Model Inputs","text":"
The table below contains brief descriptions of the input files required to execute the SANDAG ABM3.
File Name Purpose File Type Prepared By Land Use mgra_based_input{SCENARIO_YEAR}.csv Land use forecast of the size and structure of the region\u2019s economy and corresponding demographic forecast CSV Land Use Modelers, Transportation Modelers, and GIS activity_code_indcen_acs.csv PECAS activity code categories mapping to Census industry codes; This is used for military occupation mapping. CSV Land Use Modelers pecas_occ_occsoc_acs.csv PECAS activity code categories mapping to Census industry codes CSV Lande Use Modelers mobilityHubMGRA.csv CSV Transportation Modelers Synthetic Population households.csv Synthetic households CSV Transportation Modelers persons.csv Synthetic persons CSV Transportation Modelers Network: Highway (to be updated with TNED) hwycov.e00 Highway network nodes from GIS ESRI input exchange Transportation Modelers hwycov.e00 Highway network links from GIS ESRI input exchange Transportation Modelers turns.csv Highway network turns file CSV Transportation Modelers LINKTYPETURNS.dbf Highway network link type turns table DBF Transportation Modelers LINKTYPETURNSCST.DBF DBF Transportation Modelers vehicle_class_toll_factors.csv Relative toll values by six vehicle classes by Facility name. Used to identify \u201cfree for HOV\u201d type managed lane facilities. CSV Transportation Modelers off_peak_toll_factors.csv Relative toll values for the three off-peak times-of-day (EA, MD, EV) by Facility name. Multiplied together with the values from vehicle_class_toll_factors.csv to get the final toll. CSV Transportation Modelers vehicle_class_availability.csv The availability / unavailability of six vehicle classes for five times-of-day by facility name. CSV Transportation Modelers Network: Transit (To be updated with TNED) trcov.e00 Transit network arc data from GIS ESRI input exchange Transportation Modelers trcov.e00 Transit network node data from GIS ESRI input exchange Transportation Modelers trlink.csv Transit route with a list of links file CSV Transportation Modelers trrt.csv Transit route attribute file CSV Transportation Modelers trstop.csv Transit stop attribute file TCSV Transportation Modelers mode5tod.csv Transit mode parameters table CSV Transportation Modelers timexfer_XX.csv Transit timed transfers table between COASTER and feeder buses; XX is the TOD (EA, AM, MD, PM, and EV) CSV Transportation Modelers special_fares.txt Fares to coaster Text File Transportation Modelers Network: Active Transportation SANDAG_Bike_Net.dbf Bike network links DBF GIS SANDAG_Bike_Node.dbf Bike network nodes DBF GIS bikeTazLogsum.csv (not saved in inputs, instead, run at the beginning of a model run) Bike TAZ logsum CSV Transportation Modelers bikeMgraLogsum.csv (not saved in inputs, instead, run at the beginning of a model run) Bike MGRA logsum CSV Transportation Modelers walkMgraEquivMinutes.csv (not saved in inputs, instead, run at the beginning of a model run) Walk, in minutes, between MGRAs CSV Visitor Model (Derived from visitor survey) visitor_businessFrequency.csv Visitor model tour frequency distribution for business travelers CSV Transportation Modelers visitor_personalFrequency.csv Visitor model tour frequency distribution for personal travelers CSV Transportation Modelers visitor_partySize.csv Visitor model party size distribution CSV Transportation Modelers visitor_autoAvailable.csv Visitor model auto availability distribution CSV Transportation Modelers visitor_income.csv Visitor model income distribution CSV Transportation Modelers visitor_tourTOD.csv Visitor model tour time-of-day distribution CSV Transportation Modelers visitor_stopFrequency.csv Visitor model stop frequency distribution CSV Transportation Modelers visitor_stopPurpose.csv Visitor model stop purpose distribution CSV Transportation Modelers visitor_outboundStopDuration.csv Visitor model time-of-day offsets for outbound stops CSV Transportation Modelers visitor_inboundStopDuration.csv Visitor model time-of-day offsets for inbound stops CSV Transportation Modelers Airport Model (Derived from airport survey) airport_purpose.csv Airport model tour purpose frequency table CSV Transportation Modelers airport_party.csv Airport model party type frequency table CSV Transportation Modelers airport_nights.csv Airport model trip duration frequency table CSV Transportation Modelers airport_income.csv Airport model trip income distribution table CSV Transportation Modelers airport_departure.csv Airport model time-of-day distribution for departing trips CSV Transportation Modelers airport_arrival.csv Airport model time-of-day distribution for arriving trips CSV Transportation Modelers Cross-Border Model (Derived from cross-border survey) crossBorder_tourPurpose_control.csv CSV crossBorder_tourPurpose_nonSENTRI.csv Cross Border Model tour purpose distribution for Non-SENTRI tours CSV Transportation Modelers crossBorder_tourPurpose_SENTRI.csv Cross Border Model tour purpose distribution for SENTRI tours CSV Transportation Modelers crossBorder_tourEntryAndReturn.csv Cross Border Model tour entry and return time-of-day distribution CSV Transportation Modelers crossBorder_supercolonia.csv Cross Border Model distance from Colonias to border crossing locations CSV Transportation Modelers crossBorder_pointOfEntryWaitTime.csv Cross Border Model wait times at border crossing locations table CSV GIS - Pat L vtsql crossBorder_stopFrequency.csv Cross Border Model stop frequency data CSV Transportation Modelers crossBorder_stopPurpose.csv Cross Border Model stop purpose distribution CSV Transportation Modelers crossBorder_outboundStopDuration.csv Cross Border Model time-of-day offsets for outbound stops CSV Transportation Modelers crossBorder_inboundStopDuration.csv Cross Border Model time-of-day offsets for inbound stops CSV Transportation Modelers External Models (Derived from SCAG survey) externalExternalTripsByYear.csv (raw inputs have these by year) External origin-destination station trip matrix CSV Transportation Modelers externalInternalControlTotalsByYear.csv (raw inputs have these by year) External Internal station control totals read by GISDK CSV Transportation Modelers internalExternal_tourTOD.csv Internal-External Model tour time-of-day frequency distribution CSV Transportation Modelers Commercial Vehicle Model (TO BE UPDATED) tazcentroids_cvm.csv Zone centroid coordinates in state plane feet and albers CSV Transportation Modelers commVehFF.csv Commercial Vehicle Model friction factors CSV Transportation Modelers OE.csv Commercial vehicle model parameters file for off-peak early (OE) period CSV Transportation Modelers AM.csv Commercial vehicle model parameters file for AM period CSV Transportation Modelers MD.csv Commercial vehicle model parameters file for mid-day (MD) period CSV Transportation Modelers PM.csv Commercial vehicle model parameters file for PM period CSV Transportation Modelers OL.csv Commercial vehicle model parameters file for off-peak late (OL) period CSV Transportation Modelers FA.csv Commercial vehicle model parameters file for fleet allocator (FA) industry CSV Transportation Modelers GO.csv Commercial vehicle model parameters file for government/ office (GO) industry CSV Transportation Modelers IN.csv Commercial vehicle model parameters file for industrial (IN) industry CSV Transportation Modelers FA.csv Commercial vehicle model parameters file for fleet allocator (FA) industry CSV Transportation Modelers RE.csv Commercial vehicle model parameters file for retail (RE) industry CSV Transportation Modeler SV.csv Commercial vehicle model parameters file for service (SV) industry CSV Transportation Modelers TH.csv Commercial vehicle model parameters file transport and handling (TH) industry CSV Transportation Modelers WH.csv Commercial vehicle model parameters file wholesale (WH) industry CSV Transportation Modelers Truck Model TruckTripRates.csv Truck model data: Truck trip rates CSV Transportation Modelers regionalEItrips.csv Truck model data: Truck external to internal data CSV Transportation Modelers regionalIEtrips.csv Truck model data: Truck internal to external data CSV Transportation Modelers regionalEEtrips.csv Truck model data: Truck external to external data CSV Transportation Modelers specialGenerators.csv Truck model data: Truck special generator data CSV Transportation Modelers Other parametersByYears.csv Parameters by scenario years. Includes AOC, aiport enplanements, cross-border tours, cross-border sentri share. CSV Transportation Modelers filesByYears.csv File names by scenario years. CSV Transportation Modelers trip_XX.omx Warm start trip table; XX is the TOD (EA, AM, MD, PM, and EV) OMX Transportation Modelers zone.term TAZ terminal times Space Delimited Text File Transportation Modelers
MGRA_BASED_INPUT<<SCENARIO_YEAR>>.CSV
Column name Description mgra MGRANumber taz TAZ Number luz_id pop total population hhp total household population (exclude gq pop) hs housing structures hs_sf single family structures hs_mf multi family structures hs_mh mobile homes hh total number of households hh_sf number of households - single family hh_mf number of households - multi family hh_mh number of mobile homes hhs household size gq_civ GQ civilian gq_mil GQ military i1 Number of households with income less than \\(15,000 (\\)2010) i2 Number of households with income \\(15,000-\\)29,999 ($2010) i3 Number of households with income \\(30,000-\\)44,999 ($2010) i4 Number of households with income \\(45,000-\\)59,999 ($2010) i5 Number of households with income \\(60,000-\\)74,999 ($2010) i6 Number of households with income \\(75,000-\\)99,999 ($2010) i7 Number of households with income \\(100,000-\\)124,999 ($2010) i8 Number of households with income \\(125,000-\\)149,999 ($2010) i9 Number of households with income \\(150,000-\\)199,999 ($2010) i10 Number of households with income \\(200,000 or more (\\)2010) emp_gov Government employment emp_mil military employment emp_ag_min Agriculture and mining employment emp_bus_svcs Professional and Business Services employment emp_fin_res_mgm Financial and resource management employment emp_educ Education services employment emp_hlth Health services employment emp_ret Retail services employment emp_trn_wrh Transportation and Warehousing employment emp_con Construction employment emp_utl Utilities office support employment emp_mnf Manufacturing employment emp_whl Wholesale employment emp_ent Entertainment services employment emp_accm Hotel and accomodation services emp_food Food services employment emp_oth Other employment emp_non_ws_wfh Non-wage and salary work from home employments emp_non_ws_oth Non-wage and salary other employments emp_total Total employment pseudomsa Pseudo MSA - 1: Downtown 2: Central 3: North City 4: South Suburban 5: East Suburban 6: North County West 7: North County East 8: East County zip09 2009 Zip Code enrollgradekto8 Grade School K-8 enrollment enrollgrade9to12 Grade School 9-12 enrollment collegeenroll Major College enrollment othercollegeenroll Other College enrollment hotelroomtotal Total number of hotel rooms parkactive Acres of Active Park openspaceparkpreserve Acres of Open Park or Preserve beachactive Acres of Active Beach district27 milestocoast Distance (miles) to the nearest coast acres Total acres in the mgra (used in CTM) land_acres Acres of land in the mgra (used in CTM) effective_acres Effective acres in the mgra (used in CTM) truckregiontype exp_hourly Expected hourly prking cost exp_daily Expected daily prking cost exp_monthly Expected monthly prking cost parking_type MGRA parking type parking_spaces MGRA estimated parking spaces ech_dist Elementary school district hch_dist High school district remoteAVParking Remote AV parking available at MGRA: 0 = Not available, 1 = Available refueling_stations Number of refueling stations at MGRA MicroAccessTime Micro-mobility access time (mins) microtransit microtransit access time (mins) nev Neighborhood Electric Vehicle access time (mins) totint Total intersections duden Dwelling unit density empden Employment density popden Population density retempden Retail employment density totintbin Total intersection bin empdenbin Employment density bin dudenbin Dwelling unit density bin PopEmpDenPerMi Population and employment density per mile "},{"location":"inputs.html#activity-mapping-to-industry-codes","title":"Activity Mapping to Industry Codes","text":""},{"location":"inputs.html#activity_code_indcen_acscsv","title":"ACTIVITY_CODE_INDCEN_ACS.CSV","text":"Column Name Description indcen Industry code defined in PECAS: They are about 270 industry categories grouped by 6-digit NAICS code (North American Industrial Classification System) activity_code Activity code defined in PECAS. They are about 30 types of activities grouped by the industry categories:1 = Agriculture3 = Construction Non-Building office support (including mining)5 = Utilities office support9 = Manufacturing office support10 = Wholesale and Warehousing11 = Transportation Activity12 = Retail Activity13 = Professional and Business Services14 = Professional and Business Services (Building Maintenance)16 = Private Education Post-Secondary (Post K-12) and Other17 = Health Services18 = Personal Services Office Based19 = Amusement Services20 = Hotels and Motels21 = Restaurants and Bars22 = Personal Services Retail Based23 = Religious Activity24 = Private Households25 = State and Local Government Enterprises Activity27 = Federal Non-Military Activity28 = Federal Military Activity30 = State and Local Government Non-Education Activity office support31 = Public Education"},{"location":"inputs.html#pecas-soc-defined-occupational-codes","title":"PECAS SOC - Defined Occupational Codes","text":""},{"location":"inputs.html#pecas_occ_occsoc_acscsv","title":"PECAS_OCC_OCCSOC_ACS.CSV","text":"Column Name Description occsoc5 Detailed occupation codes defined by the Standard Occupational Classification (SOC) system commodity_id Commodity code defined in PECAS. The detailed SOC occupations are grouped into 6 types of laborers, which are included as part of commodity: 51 = Services Labor 52 = Work at Home Labor 53 = Sales and Office Labor 54 = Natural Resources Construction and Maintenance Labor 55 = Production Transportation and Material Moving Labor 56 = Military Labor"},{"location":"inputs.html#listing-of-external-zones-attributes","title":"Listing of External Zones Attributes","text":""},{"location":"inputs.html#externalzonesxls","title":"EXTERNALZONES.XLS","text":"Column Name Description Internal Cordon LUZ Internal Cordon Land use zone External LUZ External land use zone Cordon Point Cordon Point description Destination Approximation Name of approximate city destination Miles to be Added to Cordon Point Miles to be added to cordon point Travel Time Travel time to external zone Border Delay Border delay time Minutes to be Added to Cordon Point Minutes to be added to cordon point MPH Average miles per hour based on miles and minutes to be added to cordon point"},{"location":"inputs.html#population-synthesizer-household-data","title":"Population Synthesizer Household Data","text":""},{"location":"inputs.html#householdscsv","title":"HOUSEHOLDS.CSV","text":"Column Name Description hhid Unique Household ID household_serial_no Household serial number taz TAZ of household mgra MGRA of household hinccat1 Household income category:1 = <$30k2 = $30-60k3 = $60-100k4 = $100-150k5 = $150k+ hinc Household income num_workers Number of workers in household veh Number of vehicles in household persons Number of persons in household hht Household/family type:0 = Not in universe (vacant or GQ)1 = Family household: married-couple2 = Family household: male householder, no wife present3 = Family household: female householder, no husband present4 = Nonfamily household: male householder, living alone5 = Nonfamily household: male householder, not living alone6 = Nonfamily household: female householder, living alone7 = Nonfamily household: female householder, not living alone bldgsz Building size - Number of Units in Structure & Quality:1 = Mobile home or trailer2 = One-family house detached3 = One-family house attached8 = 20-49 Apartments9 = 50 or more apartments unittype Household unit type:0 = Non-GQ Household1 = GQ Household version Synthetic population run version. Presently set to 0. poverty Poverty indicator utilized for social equity reports. Percentage value where value <= 2 (200% of the Federal Poverty Level) indicates household is classified under poverty."},{"location":"inputs.html#population-synthesizer-person-data","title":"Population Synthesizer Person Data","text":""},{"location":"inputs.html#personscsv","title":"PERSONS.CSV","text":"Column Name Description hhid Household ID perid Person ID Household_serial_no Household serial number pnum Person Number age Age of person sex Gender of person1 = Male2 = Female military Military status of person:0 = N/A Less than 17 Years Old1 = Yes, Now on Active Duty pemploy Employment status of person:1 = Employed Full-Time2 = Employed Part-Time3 = Unemployed or Not in Labor Force4 = Less than 16 Years Old pstudent Student status of person:1 = Pre K-122 = College Undergrad+Grad and Prof. School3 = Not Attending School ptype Person type:1 = Full-time Worker2 = Part-time Worker3 = College Student4 = Non-working Adult5 = Non-working Senior6 = Driving Age Student7 = Non-driving Student8 = Pre-school educ Educational attainment:1 = No schooling completed9 = High school graduate13 = Bachelor\u2019s degree grade School grade of person:0 = N/A (not attending school)2 = K to grade 85 = Grade 9 to grade 126 = College undergraduate occen5 Occupation:0 = Not in universe (Under 16 years or LAST-WRK = 2)1..997 = Legal census occupation code occsoc5 Detailed occupation codes defined by the Bureau of Labor Statistics"},{"location":"inputs.html#highway-network-vehicle-class-toll-factors-file","title":"Highway Network Vehicle Class Toll Factors File","text":""},{"location":"inputs.html#vehicle_class_toll_factorscsv","title":"vehicle_class_toll_factors.csv","text":"
Required file. Used to specify the relative toll values by six vehicle classes by Facility name, scenario year and time of day. Can be used, for example, to identify \u201cfree for HOV\u201d type managed lane facilties. Used by the Import network Modeller tool.
Example:
Facility_name Year Time_of_Day DA_Factor S2_Factor S3_Factor TRK_L_Factor TRK_M_Factor TRK_H_Factor I-15 2016 EA 1.0 0.0 0.0 1.0 1.03 2.33 SR-125 2016 ALL 1.0 1.0 1.0 1.0 1.03 2.33 I-5 2035 ALL 1.0 1.0 0.0 1.0 1.03 2.33
The toll values for each class on each link are calculated by multiplying the input toll value from hwycov.e00 (ITOLLA, ITOLLP, ITOLLO) by this factor, matched by the Facility name (together with the toll factors from off_peak_toll_factors.csv in converting ITOLLO to the off-peak times-of-day).
The network links are matched to a record in this file based on the NM, FXNM or TXNM values (in that order). A simple substring matching is used, so the record with Facility_name \u201cI-15\u201d matches any link with name \u201cI-15 SB\u201d, \u201cI-15 NB\u201d, \u201cI-15/DEL LAGO DAR NB\u201d etc. The records should not be overlapping: if there are two records which match a given link it will be an arbitrary choice as to which one is used.
Note that if a link does not match to a record in this file, the default factors (specified in the table below) will be applied to said link. It is OK if there are records for which there are no link tolls.
Column Name Description Facility_name Name of the facility, used in the substring matching with links by NM, FXNM or TXNM fields Year Scenario year Time_of_Day Time of day period: EA = Early morning (3am - 5:59am) AM = AM peak (6am to 8:59am) MD = Mid-day (9am to 3:29pm) PM = PM peak (3:30pm to 6:59pm) EV = Evening (7pm to 2:59am) ALL = All time of day periods DA_Factor Positive toll factor for Drive Alone (SOV) vehicle classes. The default value is 1.0 S2_Factor Positive toll factor for Shared 2 person (HOV2) vehicle classes. The default value is 1.0 S3_Factor Positive toll factor for Shared 3+ person (HOV3) vehicle classes. The default value is 1.0 TRK_L_Factor Positive toll factor for Light Truck (TRKL) vehicle classes. The default value is 1.0 TRK_M_Factor Positive toll factor for Medium Truck (TRKM) vehicle classes. The default value is 1.03 TRK_H_Factor Positive toll factor for Heavy Truck (TRKH) vehicle classes. The default value is 2.03 "},{"location":"inputs.html#highway-network-off-peak-toll-factors-file","title":"Highway Network Off-Peak Toll Factors File","text":""},{"location":"inputs.html#off_peak_toll_factorscsv","title":"off_peak_toll_factors.csv","text":"
Optional file. Used to specify different tolls in the off-peak time-of-day scenarios based on the single link ITOLLO field, together with the tolls by vehicle class from vehicle_class_toll_factors.csv. Used by the Import network Modeller tool.
See note re: network link matching under vehicle_class_toll_factors.csv. Note that all facilities need not be specified, links not matched will use a factor of 1.0.
Column Name Description Facility_name Name of the facility, used in the substring matching with links by NM, FXNM or TXNM fields OP_EA_FACTOR Positive toll factor for Early AM period tolls OP_MD_FACTOR Positive toll factor for Midday period tolls OP_EV_FACTOR Positive toll factor for Evening period tolls "},{"location":"inputs.html#highway-network-vehicle-class-toll-factors-file_1","title":"Highway Network Vehicle Class Toll Factors File","text":""},{"location":"inputs.html#vehicle_class_availabilitycsv","title":"vehicle_class_availability.csv","text":"
Optional file. Specifies the availability / unavailability of six vehicle classes for five times-of-day by Facility name. This will override any mode / vehicle class availability specified directly on the network (hwycov.e00), via ITRUCK and IHOV fields. Used in the generation of time-of-day Emme scenarios in the Master run Modeller tool.
See note re: network link matching under vehicle_class_toll_factors.csv. Note that all facilities need not be specified, links not matched will use the availability as indicated by the link fields in hwycov.e00.
Column Name Description Facility_name Name of the facility, used in the substring matching with links by NM, FXNM or TXNM fields Vehicle_class Name of the vehicle class, one of DA, S2, S3, TRK_L, TRK_M, or TRK_H EA_Avail For this facility and vehicle class, is available for Early AM period (0 or 1) AM_Avail For this facility and vehicle class, is available for AM Peak period (0 or 1) MD_Avail For this facility and vehicle class, is available for Midday period (0 or 1) PM_Avail For this facility and vehicle class, is available for PM Peak period (0 or 1) EV_Avail For this facility and vehicle class, is available for Evening period (0 or 1)"},{"location":"inputs.html#special_farestxt","title":"special_fares.txt","text":"
"},{"location":"inputs.html#transit-binary-stop-table","title":"Transit Binary Stop Table","text":""},{"location":"inputs.html#trstopcsv","title":"TRSTOP.CSV","text":"Column Name Description Stop_id Unique stop ID Route_id Sequential route number Link_id Link id associated with route Pass_count Number of times the route passes this stop. Most of value is one, some value is 2 Milepost Stop mile post Longitude Stop Longitude Latitude Stop Latitude NearNode Node number that stop is nearest to FareZone Zones defined in Fare System StopName Name of Stop MODE_NAME Line haul mode name: Transfer Center City Walk Walk Access Commuter Rail Light Rail Regional BRT (Yellow) Regional BRT (Red) Limited Express Express Local MODE_ID Mode ID 1 = Transfer 2 = Center City Walk 3 = Walk Access 4 = Commuter Rail 5 = Light Rail 6 = Regional BRT (Yellow) 7 = Regional BRT (Red) 8 = Limited Express 9 = Express 10 = Local PREMODE Premium Transit mode 0 = No 1 = Yes EXPBSMODE Express bus mode 0 = No 1 = Yes LOCMODE Local bus mode 0 = No 1 = Yes OP_TRNTIME Off peak transcad matrix used by mode: *oploctime *oppretime AM_TRNTIME AM peak transcad matrix used by mode: *amloctime *ampretime PM_TRNTIME PM peak transcad matrix used by mode: *pmloctime *pmpretime MODE_ACCES Mode of access (1) MODE_EGRES Mode of egress (1) WT_IVTPK Weight for peak in-vehicle time: 1, 1.5, or 1.8 WT_FWTPK Weight for peak first wait time: 1, 1.5 WT_XWTPK Weight for peak transfer wait time: 1, 3 WT_FAREPK Weight for peak fare: 0.46, 0.60, 0.63, 0.67, 1 WT_IVTOP Weight for off-peak in-vehicle time: 1, 1.5, or 1.6 WT_FWTOP Weight for off-peak first wait time: 1, 1.5 WT_XWTOP Weight for off-peak transfer wait time: 1, 3 WT_FAREOP Weight for off-peak fare: 0.23, 0.51, 0.52, 0.54, 0.58, 1 FARE Transit fare: $0, $1.25, $1.50, $2.50, $3.00, $3.50 DWELLTIME Dwell time: 0, 0.3, 0.5 FARETYPE Fare Type: 1 = Bus 2 = Rail FAREFIELD Fare Field: coaster fare lightrail fare CRMODE Boolean if Commuter rail available LRMODE Boolean if light rail available XFERPENTM Transfer Penalty time: 5 minutes WTXFERTM Transfer Wait time: 1 minute TRNTIME_EA Early AM transit time impedance TRNTIME_AM AM transit time impedance TRNTIME_MD Midday transit time impedance TRNTIME_PM PM transit time impedance TRNTIME_EV Evening transit time impedance"},{"location":"inputs.html#transit-timed-transfers-between-coaster-and-feeder-buses","title":"Transit Timed Transfers Between COASTER and Feeder Buses","text":""},{"location":"inputs.html#timexfer_xxcsv","title":"TIMEXFER_XX.CSV","text":"Column Name Description FROM_LINE From Route Number TO_LINE To Route Number WAIT_TIME Wait time in minutes"},{"location":"inputs.html#transit-stop-table","title":"Transit Stop Table","text":""},{"location":"inputs.html#trstopcsv_1","title":"TRSTOP.CSV","text":"Column Name Description Stop_id Unique stop ID Route_id Sequential route number Link_id Link id associated with route Pass_count Number of times the route passes this stop. Most of value is one, some value is 2 Milepost Stop mile post Longitude Stop Longitude Latitude Stop Latitude NearNode Node number that stop is nearest to FareZone Zones defined in Fare System StopName Name of Stop MODE_NAME Line haul mode name:TransferCenter City WalkWalk AccessCommuter RailLight RailRegional BRT (Yellow)Regional BRT (Red)Limited ExpressExpressLocal MODE_ID Mode ID1 = Transfer2 = Center City Walk3 = Walk Access4 = Commuter Rail5 = Light Rail6 = Regional BRT (Yellow)7 = Regional BRT (Red)8 = Limited Express9 = Express10 = Local PREMODE Premium Transit mode0 = No1 = Yes EXPBSMODE Express bus mode0 = No1 = Yes LOCMODE Local bus mode0 = No1 = Yes OP_TRNTIME Off peak transcad matrix used by mode:oploctimeoppretime AM_TRNTIME AM peak transcad matrix used by mode:amloctimeampretime PM_TRNTIME PM peak transcad matrix used by mode:pmloctimepmpretime MODE_ACCES Mode of access (1) MODE_EGRES Mode of egress (1) WT_IVTPK Weight for peak in-vehicle time: 1, 1.5, or 1.8 WT_FWTPK Weight for peak first wait time: 1, 1.5 WT_XWTPK Weight for peak transfer wait time: 1, 3 WT_FAREPK Weight for peak fare: 0.46, 0.60, 0.63, 0.67, 1 WT_IVTOP Weight for off-peak in-vehicle time: 1, 1.5, or 1.6 WT_FWTOP Weight for off-peak first wait time: 1, 1.5 WT_XWTOP Weight for off-peak transfer wait time: 1, 3 WT_FAREOP Weight for off-peak fare: 0.23, 0.51, 0.52, 0.54, 0.58, 1 FARE Transit fare: $0, $1.25, $1.50, $2.50, $3.00, $3.50 DWELLTIME Dwell time: 0, 0.3, 0.5 FARETYPE Fare Type:1 = Bus2 = Rail FAREFIELD Fare Field:coaster farelightrail fare CRMODE Boolean if Commuter rail available LRMODE Boolean if light rail available XFERPENTM Transfer Penalty time: 5 minutes WTXFERTM Transfer Wait time: 1 minute TRNTIME_EA Early AM transit time impedance TRNTIME_AM AM transit time impedance TRNTIME_MD Midday transit time impedance TRNTIME_PM PM transit time impedance TRNTIME_EV Evening transit time impedance"},{"location":"inputs.html#transit-link-file","title":"Transit Link File","text":""},{"location":"inputs.html#trlinkcsv","title":"TRLINK.CSV","text":"Column Name Description Route_id: Sequential route number Link_id Link id associated with route Direction + or -"},{"location":"inputs.html#bike-network-link-field-list","title":"Bike Network Link Field List","text":""},{"location":"inputs.html#sandag_bike_netdbf","title":"SANDAG_BIKE_NET.DBF","text":"Column Name Description ROADSEGID Road Segment ID RD20FULL Road/Street Name A Foreign key of first node B Foreign key of second node A_LEVEL Level of first node B_LEVEL Level of second node Distance Arc length of link (ft) AB_Gain Cumulative non-negative increase in elevation from A to B nodes (ft) BA_Gain Cumulative non-negative increase in elevation from B to A nodes (ft) ABBikeClas Type of Bike Classification in AB direction where:1 = Multi-Use Path2 = Bike Lane3 = Bike Route BABikeClas Type of Bike Classification in BA direction where:1 = Multi-Use Path2 = Bike Lane3 = Bike Route AB_Lanes Number of Lanes in AB direction BA_Lanes Number of Lanes in BA direction Func_Class Type of Road Functional Class where:1 = Freeway to Freeway Ramp2 = Light (2-lane) Collector Street3 = Rural Collector Road4 = Major Road/4-lane Major Road5 = Rural Light Collector/Local Road6 = Prime Arterial7 = Private Street8 = Recreational Parkway9 = Rural Mountain RoadA = AlleyB = Class I Bicycle PathC = Collector/4-lane Collector StreetD = Two-lane Major StreetE = ExpresswayF = FreewayL = Local Street/Cul-de-sacM = Military Street within BaseP = Paper StreetQ = UndocumentedR = Freeway/Expressway On/Off RampS = Six-lane Major StreetT = TransitwayU = Unpaved RoadW = Pedestrian Way/Bikeway Bike2Sep Separated Bike Lane Flag where:0 = No1 = Yes Bike3Blvd Bike Boulevard Lane Flag where:0 = No1 = Yes SPEED Road Speed A_Elev A Node Elevation B_Elev B Node Elevation ProjectID Project ID in the regional bike network Year Year built/opened to the public Scenicldx Scenic index represents the closeness to the ocean and parks Path Null Shape_Leng length of the link (ft)"},{"location":"inputs.html#bike-network-node-field-list","title":"Bike Network Node Field List","text":""},{"location":"inputs.html#sandag_bike_nodedbf","title":"SANDAG_BIKE_NODE.DBF","text":"Column Name Description NodeLev_ID Node Unique Identifier MGRA MGRA ID for Centroids TAZ TAZ ID for Centroids TAP TAP ID XCOORD X Coordinate of Node in NAD 1983 State Plane California Region VI FIPS: 0406 (ft) YCOORD Y Coordinate of Node in NAD 1983 State Plane California Region VI FIPS: 0406(ft) ZCOORD Elevation (ft) Signal Traffic Signal Presence where: 0 = Absence 1 = Presence"},{"location":"inputs.html#zone-terminal-time","title":"Zone Terminal Time","text":""},{"location":"inputs.html#zoneterm","title":"ZONE.TERM","text":"Column Name Description Zone TAZ number Terminal time Terminal time (3, 4, 5, 7, 10 minutes)"},{"location":"inputs.html#bike-taz-logsum","title":"Bike TAZ Logsum","text":""},{"location":"inputs.html#biketazlogsumcsv","title":"BIKETAZLOGSUM.CSV","text":"Column Name Description i Origin TAZ j Destination TAZ Logsum Logsum - a measure of the closeness of the origin and the destination of the trip time Time (In minutes)"},{"location":"inputs.html#bike-mgra-logsum","title":"Bike MGRA Logsum","text":""},{"location":"inputs.html#bikemgralogsumcsv","title":"BIKEMGRALOGSUM.CSV","text":"Column Name Description i Origin of MGRA j Destination of MGRA Logsum Logsum - a measure of the closeness of the origin and the destination of the trip time Time (in minutes)"},{"location":"inputs.html#walk-mgra-equivalent-minutes","title":"Walk MGRA Equivalent Minutes","text":""},{"location":"inputs.html#walkmgraequivminutescsv","title":"WALKMGRAEQUIVMINUTES.CSV","text":"Column Name Description i Origin (MGRA) j Destination (MGRA) percieved Percieved time to walk actual Actual time to walk (minutes) gain Gain in elevation"},{"location":"inputs.html#airport-trip-purpose-distribution","title":"Airport Trip Purpose Distribution","text":""},{"location":"inputs.html#airport_purposesancsv-and-airport_purposecbxcsv","title":"AIRPORT_PURPOSE.SAN.CSV AND AIRPORT_PURPOSE.CBX.CSV","text":"Column Name Description Purpose Trip Purpose: 0 = Resident Business 1 = Resident Personal 2 = Visitor Business 3 = Visitor Personal 4 = External Percent Distribution of Trips in trip purpose"},{"location":"inputs.html#airport-party-size-by-purpose-distribution","title":"Airport Party Size by Purpose Distribution","text":""},{"location":"inputs.html#airport_partysancsv-and-airport_partycbxcsv","title":"AIRPORT_PARTY.SAN.CSV AND AIRPORT_PARTY.CBX.CSV","text":"Column Name Description Party Party size (0 through 5+) purp0_perc Distribution for Resident Business purpose purp1_perc Distribution for Resident Personal purpose purp2_perc Distribution for Visitor Business purpose purp3_perc Distribution for Visitor Personal purpose purp4_perc Distribution for External purpose"},{"location":"inputs.html#airport-number-of-nights-by-purpose-distribution","title":"Airport Number of Nights by Purpose Distribution","text":""},{"location":"inputs.html#airport_nightssancsv-and-airport_nightscbxcsv","title":"AIRPORT_NIGHTS.SAN.CSV AND AIRPORT_NIGHTS.CBX.CSV","text":"Column Name Description Nights Number of Nights stayed (0 through 14+) purp1_perc Distribution for Resident Personal purpose purp2_perc Distribution for Visitor Business purpose purp3_perc Distribution for Visitor Personal purpose purp4_perc Distribution for External purpose"},{"location":"inputs.html#airport-income-by-purpose-distribution","title":"Airport Income by Purpose Distribution","text":""},{"location":"inputs.html#airport_incomesancsv-and-airport_incomecbxcsv","title":"AIRPORT_INCOME.SAN.CSV AND AIRPORT_INCOME.CBX.CSV","text":"Column Name Description Income group Household income: 0 = Less than $25K 1 = $25K \u2013 $50K 2 = $50K \u2013 $75K 3 = $75K \u2013 $100K 4 = $100K \u2013 $125K 5 = $125K \u2013 $150K 6 = $150K \u2013 $200K 7 = $200K plus purp1_perc Distribution for Resident Personal purpose purp2_perc Distribution for Visitor Business purpose purp3_perc Distribution for Visitor Personal purpose purp4_perc Distribution for External purpose"},{"location":"inputs.html#airport-departure-time-by-purpose-distribution","title":"Airport Departure Time by Purpose Distribution","text":""},{"location":"inputs.html#airport_departuresancsv-and-airport_departurecbxcsv","title":"AIRPORT_DEPARTURE.SAN.CSV and AIRPORT_DEPARTURE.CBX.CSV","text":"Column Name Description Period Departure Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM purp1_perc Distribution for Resident Personal purpose purp2_perc Distribution for Visitor Business purpose purp3_perc Distribution for Visitor Personal purpose purp4_perc Distribution for External purpose"},{"location":"inputs.html#airport-arrival-time-by-purpose-distribution","title":"Airport Arrival Time by Purpose Distribution","text":""},{"location":"inputs.html#airport_arrivalsancsv-and-airport_arrivalcbxcsv","title":"AIRPORT_ARRIVAL.SAN.CSV and AIRPORT_ARRIVAL.CBX.CSV","text":"Column Name Description Period Arrival Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM purp1_perc Distribution for Resident Personal purpose purp2_perc Distribution for Visitor Business purpose purp3_perc Distribution for Visitor Personal purpose purp4_perc Distribution for External purpose"},{"location":"inputs.html#cross-border-model-tour-entry-and-return-distribution","title":"Cross Border Model Tour Entry and Return Distribution","text":""},{"location":"inputs.html#crossborder_tourentryandreturncsv","title":"CROSSBORDER_TOURENTRYANDRETURN.CSV","text":"Column Name Description Purpose Tour Purpose: 0 = Work 1 = School 2 = Cargo 3 = Shop 4 = Visit 5 = Other EntryPeriod Entry Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM Return Period Return Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM Percent Distribution of tours in entry and return period time slots"},{"location":"inputs.html#cross-border-model-supercolonia","title":"Cross Border Model Supercolonia","text":""},{"location":"inputs.html#crossborder_supercoloniacsv","title":"CROSSBORDER_SUPERCOLONIA.CSV","text":"Column Name Description Supercolonia_ID Super colonia ID Population Population of the super colonia Distance_poe0 Distance from colonia to point of entry 0 (San Ysidro) Distance_poe1 Distance from colonia to point of entry 1 (Otay Mesa) Distance_poe2 Distance from colonia to point of entry 2 (Tecate)"},{"location":"inputs.html#cross-border-model-point-of-entry-wait-time","title":"Cross Border Model Point of Entry Wait Time","text":""},{"location":"inputs.html#crossborder_pointofentrywaittimecsv","title":"CROSSBORDER_POINTOFENTRYWAITTIME.CSV","text":"Column Name Description poe Point of Entry number: 0 = San Ysidro 1 = Otay Mesa 2 = Tecate 3 = Otay Mesa East 4 = Jacumba StartHour Start Hour (1 through 12) EndHour End Hour (1 through 12) StartPeriod Start Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM EndPeriod End Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM StandardWait Standard wait time SENTRIWait SENTRI users wait time PedestrianWait Pedestrian wait time"},{"location":"inputs.html#cross-border-model-stop-frequency","title":"Cross Border Model Stop Frequency","text":""},{"location":"inputs.html#crossborder_stopfrequencycsv","title":"CROSSBORDER_STOPFREQUENCY.CSV","text":"Column Name Description Purpose Tour Purpose: 0 = Work 1 = School 2 = Cargo 3 = Shop 4 = Visit 5 = Other DurationLo Lower bound of tour duration (0, 4, or 8) DurationHi Upper bound of tour duration (4, 8, or 24) Outbound Number of stops on the outbound (0, 1, 2, 3+) Inbound Number of stops on the inbound (0, 1, 2, 3+) Percent Distribution of tours by purpose, duration, number of outbound/inbound stops"},{"location":"inputs.html#cross-border-model-stop-purpose-distribution","title":"Cross Border Model Stop Purpose Distribution","text":""},{"location":"inputs.html#crossborder_stoppurposecsv","title":"CROSSBORDER_STOPPURPOSE.CSV","text":"Column Name Description TourPurp Tour Purpose: 0 = Work 1 = School 2 = Cargo 3 = Shop 4 = Visit 5 = Other Inbound Boolean for whether stop is inbound (0=No, 1=Yes) StopNum Stop number on tour (1, 2, or 3) Multiple Boolean for whether there are multiple stops on tour (0=No, 1=Yes) StopPurp0 Distribution of Work stops StopPurp1 Distribution of School stops StopPurp2 Distribution of Cargo stops StopPurp3 Distribution of Shopping stops StopPurp4 Distribution of Visiting stops StopPurp5 Distribution of Other stops"},{"location":"inputs.html#cross-border-model-outbound-stop-duration-distribution","title":"Cross Border Model Outbound Stop Duration Distribution","text":""},{"location":"inputs.html#crossborder_outboundstopdurationcsv","title":"CROSSBORDER_OUTBOUNDSTOPDURATION.CSV","text":"Column Name Description RemainingLow Lower bound of remaining half hour periods after last scheduled trip: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM RemainingHigh Upper bound of remaining half hour periods after last scheduled trip: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM Stop Stop number on tour (1, 2, or 3) 0 Probability that stop departure is in same period as last outbound trip 1 Probability that stop departure is in last outbound trip period + 1 2 Probability that stop departure is in last outbound trip period + 2 3 Probability that stop departure is in last outbound trip period + 3 4 Probability that stop departure is in last outbound trip period + 4 5 Probability that stop departure is in last outbound trip period + 5 6 Probability that stop departure is in last outbound trip period + 6 7 Probability that stop departure is in last outbound trip period + 7 8 Probability that stop departure is in last outbound trip period + 8 9 Probability that stop departure is in last outbound trip period + 9 10 Probability that stop departure is in last outbound trip period + 10 11 Probability that stop departure is in last outbound trip period + 11"},{"location":"inputs.html#cross-border-model-inbound-stop-duration-distribution","title":"Cross Border Model Inbound Stop Duration Distribution","text":""},{"location":"inputs.html#crossborder_inboundstopdurationcsv","title":"CROSSBORDER_INBOUNDSTOPDURATION.CSV","text":"Column Name Description RemainingLow Lower bound of remaining half hour periods after last scheduled trip: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM RemainingHigh Upper bound of remaining half hour periods after last scheduled trip: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM Stop Stop number on tour (1, 2, or 3) 0 Probability that stop departure period is same as tour arrival period -1 Probability that stop departure period is tour arrival period - 1 -2 Probability that stop departure period is tour arrival period \u2013 2 -3 Probability that stop departure period is tour arrival period \u2013 3 -4 Probability that stop departure period is tour arrival period \u2013 4 -5 Probability that stop departure period is tour arrival period \u2013 5 -6 Probability that stop departure period is tour arrival period \u2013 6 -7 Probability that stop departure period is tour arrival period - 7"},{"location":"inputs.html#externalexternaltripsbyyearcsv","title":"EXTERNALEXTERNALTRIPSByYEAR.CSV","text":"Column Name Description originTAZ External origin TAZ destinationTAZ External destination TAZ Trips Number of trips between external TAZs"},{"location":"inputs.html#external-internal-control-totals","title":"External Internal Control Totals","text":""},{"location":"inputs.html#externalinternalcontroltotalsbyyearcsv","title":"EXTERNALINTERNALCONTROLTOTALSByYEAR.CSV","text":"Column Name Description Taz External TAZ station Work Number of work vehicle trips Nonwork Number of non-work vehicle trips"},{"location":"inputs.html#internal-external-tours-time-of-day-distribution","title":"Internal External Tours Time of Day Distribution","text":""},{"location":"inputs.html#internalexternal_tourtodcsv","title":"INTERNALEXTERNAL_TOURTOD.CSV","text":"Column Name Description Purpose Tour Purpose: 0 = All Purposes EntryPeriod Entry Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM ReturnPeriod Return Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM Percent Distribution of tours by entry and return periods"},{"location":"inputs.html#parameters-by-scenario-years","title":"Parameters by Scenario Years","text":""},{"location":"inputs.html#parametersbyyearscsv","title":"PARAMETERSBYYEARS.CSV","text":"Column Name Description year Scenario build year aoc.fuel Auto operating fuel cost aoc.maintenance Auto operating maitenance cost airport.SAN.enplanements San Diego International Airport enplanements airport.SAN.connecting San Diego International Airport connecting passengers airport.SAN.airportMgra MGRA San Diego International Airport is located in airport.CBX.enplanements Cross Border Express Terminal (Tijuana International Airport) enplanements airport.CBX.connecting Cross Border Express Terminal (Tijuana International Airport) connecting passengers airport.CBX.airportMgra MGRA Cross Border Express Terminal is located in crossBorder.tours Number of cross border tours crossBorders.sentriShare Share of cross border tours that are SENTRI taxi.baseFare Initial taxi fare taxi.costPerMile Taxi cost per mile taxi.cosPerMinute Taxi cost per minute TNC.single.baseFare Initial TNC fare for single ride TNC.single.costPerMile TNC cost per mile for single ride TNC.single.costPerMinute TNC cost per minute for single ride TNC.single.costMinimum TNC minimum cost for single ride TNC.shared.baseFare Initial TNC fare for shared ride TNC.shared.costPerMile TNC cost per mile for shared ride TNC.shared.costPerMinute TNC cost per minute for shared ride TNC.shared.costMinimum TNC minimum cost for shared ride Mobility.AV.RemoteParkingCostPerHour Remote parking cost per hour for autonomous vehicles active.micromobility.variableCost Variable cost for micromobility active.micromobility.fixedCost Fixed cost for micromobility active.microtransit.fixedCost Fixed cost for microtransit Mobility.AV.Share The share of vehicles assumed to be autonomous vehicles in the vehicle fleet smartSignal.factor.LC smartSignal.factor.MA smartSignal.factor.PA atdm.factor"},{"location":"inputs.html#files-by-scenario-years","title":"Files by Scenario Years","text":""},{"location":"inputs.html#filesbyyearscsv","title":"FILESBYYEARS.CSV","text":"Column Name Description year Scenario build year crossborder.dc.soa.alts.file Crossborder model destination choice alternatives file crossBorder.dc.uec.file Crossborder model destination choice UEC file uwsl.dc.uec.file Tour destination choice UEC file nmdc.uec.file Non-mandatory tour destination choice UEC file crossBorder.tour.mc.uec.file Crossborder model tour mode choice UEC file visualizer.reference.path Path to reference scenario for SANDAG ABM visualizer"},{"location":"inputs.html#mgras-at-mobility-hubs","title":"MGRAs at Mobility Hubs","text":""},{"location":"inputs.html#mobilityhubmgracsv","title":"MOBILITYHUBMGRA.CSV","text":"Column Name Decription MGRA MGRA ID MoHubName Mobility Hub name MoHubType Mobility Hub type: Suburban Coastal Gateway Major Employment Center Urban
Model outputs are stored in the .\\outputs directory. The contents of the directory are listed in the table below.
"},{"location":"outputs.html#output-directory-output","title":"Output Directory (.\\output)","text":"Directory\\File Name Description airport.CBX (directory) Outputs for Cross-Border Express Airport Ground Access Model airport.SAN (directory) Outputs for San Diego International Airport Ground Access Model assignment (directory) Assignment outputs crossborder (directory) Crossborder Travel Model outputs cvm (directory) Commercial Vehicle Model outputs parking (directory) Parking model outputs resident (directory) Resident model outputs skims (directory) Skim outputs visitor (directory) Visitor Model outputs bikeMgraLogsum.csv Bike logsum file for close-together MGRAs bikeTazLogsum.csv Bike logsum file for TAZs datalake_metadata.yaml Metadata file for datalake reporting system derivedBikeEdges.csv Derived bike network edge file derivedBikeNodes.csv Derived bike network node file derivedBikeTraversals.csv Derived bike network traversals file microMgraEquivMinutes.csv Equivalent minutes for using micromobility between close-together MGRAs (not used) runtime_summary.csv Summary of model runtime temp_tazdata_cvm.csv TAZ data for commercial vehicle model transponderModelAccessibilities.csv Transponder model accessibilities (not used) trip_(period).omx Trips for each time period, for assignment walkMgraEquivMinutes.csv Equivalent minutes for walking between close-together MGRAs"},{"location":"outputs.html#skims-skims","title":"Skims (.\\skims)","text":"
This directory contains auto, transit, and non-motorized level-of-service matrices, also known as skims. Each file is a collection of origin destination tables of times and costs, at the TAZ level.
File Description dest_pmsa.omx A matrix containing pseudo - metropolitan statistical area code for each destination TAZ dest_poi.omx A matrix containing point of interest code for each destination TAZ (currently zeros) dest_poi.omx.csv A csv file containing point of interest code for each destination TAZ (currently zeros) impm(truck type)(toll type)_(period)_(matrixtype).txt Truck impedance matrix for truck type (ld = Light duty, lhd = light heavy duty, mhd = medium heavy duty, hhd = heavy heavy duty), toll type (n = non-toll, t = toll) and matrixtype (DU = utility, dist = distance, time = time) maz_maz_bike.csv Bike logsums between close together MGRAs maz_maz_walk.csv Walk times between close together MGRAs maz_stop_walk.csv Walk times between MGRAs and transit stops taz_pmsa_xwalk.csv Crosswalk file between pseudo-metropolitan statistical areas and TAZs traffic_skims_(period).omx Auto skims by period (EA, AM, MD, PM, EV) transit_skims_(period).omx Transit skims by period (EA, AM, MD, PM, EV) "},{"location":"outputs.html#auto-skims-by-period","title":"Auto skims by period","text":"
TRAFFIC_SKIMS_<time period>.OMX
Table Name Description <time_period>_<traffic_class><_vot>_TIME Travel time for evaluation of volume-delay functions <time_period>_<traffic_class><_vot>_DIST Travel distance <time_period>_<traffic_class><_vot>_REL <time_period>_<traffic_class><_vot>_TOLLCOST Total toll cost (only for TOLL traffic classes) <time_period>_<traffic_class><_vot>_TOLLDIST Total distance on toll facilities (only for TOLL traffic classes) <time_period>_<traffic_class><_vot>_MLCOST Total cost for managed lane facilities (only for TOLL traffic classes) <time_period>_<traffic_class><_vot>_HOVDIST Distance on HOV facilities (only for HOV traffic classes) *traffic class = SOV_TR, SOV_NT, HOV2, HOV3, TRK vot = L (low), M (medium), and H (high) "},{"location":"outputs.html#transit-skims-by-period","title":"Transit skims by period","text":"
TRANSIT_SKIMS_<time_period>.OMX
Table Name Description <time_period>_<transit_class>_GENCOST Total generalized cost which includes perception factors from assignment <time_period>_<transit_class>_FIRSTWAIT actual wait time at initial boarding point <time_period>_<transit_class>_XFERWAIT actual wait time at all transfer boarding points <time_period>_<transit_class>_TOTALWAIT total actual wait time <time_period>_<transit_class>_FARE fare paid <time_period>_<transit_class>_XFERS number of transfers <time_period>_<transit_class>_ACCWALK access actual walk time prior to initial boarding <time_period>_<transit_class>_EGRWALK egress actual walk time after final alighting <time_period>_<transit_class>_TOTALWALK total actual walk time <time_period>_<transit_class>_TOTALIVTT Total actual in-vehicle travel time <time_period>_<transit_class>_DWELLTIME Total dwell time at stops <time_period>_<transit_class>_BUSIVTT actual in-vehicle travel time on local bus mode <time_period>_<transit_class>_LRTIVTT actual in-vehicle travel time on LRT mode <time_period>_<transit_class> _CMRIVTT actual in-vehicle travel time on commuter rail mode <time_period>_<transit_class> _EXPIVTT actual in-vehicle travel time on express bus mode <time_period>_<transit_class>_LTDEXPIVTT actual in-vehicle travel time on premium bus mode <time_period>_<transit_class>_BRTIVTT actual in-vehicle travel time on BRT mode *time period = EA, AM, MD, PM, EV transit_class = LOC, PRM, MIX"},{"location":"outputs.html#activitysim-log-files","title":"ActivitySim log files","text":"
ActivitySim writes out various log files when it runs; these have standard names for each model component. Therefore we list them separately, but copies of these files may be in each model\u2019s output directory depending upon the settings used to run ActivitySim for that model component.
File Description activitysim.log ActivitySim log file for model breadcrumbs.yaml Breadcrumbs provides a record of steps that have been run for use when resuming a model run final_checkpoints.csv ActivitySim checkpoint file final_pipeline.h5 ActivitySim pipeline file mem.csv ActivitySim memory use log file mem_mp_households.csv Memory logs for ActivitySim model steps running with the same num_processes (all except accessibility, initialize, and summarize) mem_mp_initialize.csv Memory logs for ActivitySim model step initialize mem_mp_summarize.csv Memory logs for ActivitySim model step summarize mp_households_(processnumber)-activitysim.log ActivitySim log file for processnumber. This logfile is created if model is run in multiprocess mode mp_households_(processnumber)-mem.csv Memory log file for processnumber mp_households_apportion-activitysim.log ActivitySim log file for apportioning data between multiple processes mp_households_coalesce-activitysim.log ActivitySIm logfile for coalesing output from multiple processes into one mp_initialize-activitysim.log ActivitySim log file for the initialization steps mp_initialize-mem.csv Memory logs for ActivitySim model step summarize (similar to mp_initialize-mem.csv) mp_setup_skims-activitysim.log ActivitySim logfile for reading in skims mp_summarize-activitysim.log ActivitySim log file for summarizing model output (omx and csv trip table) mp_summarize-mem.csv Memory logs for ActivitySim model step summarize (similar to mem_mp_initialize.csv) mp_tasks_log.txt Log files of multiprocessed steps omnibus_mem.csv Memory log file of all model steps (similar to mem.csv) run_list.txt List of models that have been run timing_log.csv Model run time by steps"},{"location":"outputs.html#airport-model-outputs-airportcbx-airportsan","title":"Airport model outputs (.\\airport.CBX, .\\airport.SAN)","text":"
There are two subdirectories containing outputs for each of the two airport models. airport.CBX contains output for the Cross-Border Express model, and airport.SAN contains output for the San Diego International Airport model. Each directory has identical files so we provide one generic output table below.
Filename Description final_(airport)accessibility.csv Accessibility file for airport (cbx, san) (not used, created by default) [final_(airport)households.csv](#### Airport Model household file (final_(airport)households.csv)) Household file for airport (cbx, san) final_(airport)land_use.csv Land-use file for airport (cbx, san) [final_(airport)persons.csv](#### Airport Model person file (final_(airport)persons.csv)) Persons file for airport (cbx, san) [final_(airport)tours.csv](#### Airport Model tour file (final_(airport)tours.csv)) Tour file for airport (cbx, san) [final_(airport)trips.csv](#### Airport Model trip file (final_(airport)trips.csv)) Trip file for airport (cbx, san) model_metadata.yaml Datalake metadata file autoairporttrips.(airport)_(period).omx Auto trip table for airport (CBX, SAN) by period (EA, AM, MD, PM, EV) tranairporttrips.(airport)_(period).omx Transit trip table for airport (CBX, SAN) by period (EA, AM, MD, PM, EV) nmotairporttrips.(airport)_(period).omx Non-motorized trip table for airport (CBX, SAN) by period (EA, AM, MD, PM, EV)"},{"location":"outputs.html#airport-model-household-file-final_airporthouseholdscsv","title":"Airport Model household file (final_(airport)households.csv)","text":"Field Description home_zone_id Airport MGRA sample_rate Sample rate household_id Household ID poverty Poverty indicator utilized for social equity reports. Percentage value where value <= 2 (200% of the Federal Poverty Level) indicates household is classified under poverty."},{"location":"outputs.html#airport-model-person-file-final_airportpersonscsv","title":"Airport Model person file (final_(airport)persons.csv)","text":"Field Description household_id Household ID person_id Person ID"},{"location":"outputs.html#airport-model-tour-file-final_airporttourscsv","title":"Airport Model tour file (final_(airport)tours.csv)","text":"Field Description tour_id Tour ID purpose_id ID for tour type:
1 = resident business
2 = resident personal
3= visitor business
4 = visitor personal
5 = external party_size Number of persons in airport travel party nights Number of nights away income Income group 0-7, -99 if employee direction Direction of trip. String. outbound: airport to non-airport, inbound: non-airport to airport household_id Household ID person_id Person ID tour_category Tour category. String \"non_mandatory\" tour_type Type of tour. String. \"Emp\": Employee, \"ext\": External, \"res_busn\": Resident business where n is the ID for the income bracket (1<25K, 2: between 25K & 50K, 3: between 50K & 75K, 4: between 75K & 100K, 5: between 100K & 125K, 6: between 125K & 150K, 7: between 150K & 200K, 8: 200k+
, \"res_pern\": Resident personal where n is the ID for the income bracket as defined above, \"vis_bus\": Visitor business, \"vis_per\": Visitor personal origin Origin MGRA destination Destination MGRA number_of_participants Same as party_size outbound TRUE if outbound, else FALSE start Half-hour time period of departure from tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. end Half-hour time period of arrival back at tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. duration Duration of the tour in number of half-hour periods, including all activity episodes and travel destination_logsum Logsum from destination choice model stop_frequency out_0in, 0out_in primary_purpose \"busn\", \"emp\", \"extn\", \"pern\""},{"location":"outputs.html#airport-model-trip-file-final_airporttripscsv","title":"Airport Model trip file (final_(airport)trips.csv)","text":"Field Description trip_id Trip ID person_id Person ID household_id Household ID primary_purpose Primary purpose of trip: \"busn\": Business, where n is..., \"emp\": Employee, \"extn\": External, where n is..., \"pern\": Personal, where n is... trip_num 1 outbound TRUE if outbound, else FALSE trip_count 1 destination Destination MGRA origin Origin MGRA tour_id Tour ID depart Departure time period (1\u202648) trip_mode Trip mode (see trip mode table) mode_choice_logsum Mode choice logsum for trip vot Value of time in dollars per hour ($2023) arrival_mode Arrival mode from airport trip mode choice model cost_parking Cost of parking ($2023) cost_fare_drive Ridehail/Taxi fare on a trip distance_walk Distance walked on a trip (including access/egress for transit modes) time_mm Micromobility time distance_mm Micromobility distance cost_fare_mm Micromobility cost ($2023) distance_bike Bike distance time_wait_drive Ridehail/Taxi wait times for a trip trip_period A string indicating the skim period for the trip (\u201cEA\u201d,\u201dAM\u201d,\u201dMD\u201d,\u201dPM\u2019,\u201dEV\u201d) party Party size tour_participants Number of joint tour participants if joint tour, else 1 distance_total Trip distance add_driver TRUE if trip requires a driver based on airport mode (for example, TNC, or pickup), else FALSE weight_trip 1 weight_person_trip weight_trip * tour_participants cost_operating_drive Auto operating cost ($2023) inbound TRUE if trip is from (origin) airport to (destination) non-airport zone, else FALSE time_drive Auto time distance_drive Auto distance cost_toll_drive Auto toll cost ($2023) time_transit_in_vehicle Transit in-vehicle time time_rapid_transit_in_vehicle Rapid transit in-vehicle time time_express_bus_transit_in_vehicle Express bus in-vehicle time time_local_bus_transit_in_vehicle Local bus in-vehicle time time_light_rail_transit_in_vehicle Light rail transit in-vehicle time time_commuter_rail_transit_in_vehicle Commuter rail in-vehicle time time_transit_initial_wait Transit initial-wait time cost_fare_transit Transit fare ($2023) transfers_transit Number of transfers time_bike Bike time time_walk Walk mode time cost_total Sum of all costs a trip might incur (auto operating, toll, transit fare) time_total Total travel time (including iIVT and access/egress and wait times for all modes) value_of_time_category_id Value of time bin. 1: Low, 2: Medium, 3: High sample_rate Sample rate otaz Origin TAZ dtaz Destination TAZ"},{"location":"outputs.html#arrival-mode-table-for-airport-models","title":"Arrival Mode Table for Airport Models","text":"Field Description Curb_LOCn Pickup/Dropoff curbside (n=1,5, with 1 = terminal, and 2,5 = other locations) TAXI_LOCn Taxi to airport (n =1,2 with 1= terminal mgra and 2=other) RIDEHAIL_LOCn Ridehail to airport (n =1,2 with 1= terminal mgra and 2=other) PARK_LOCn Parking lot (n=1,5, with 1 = terminal mgra and 2,5= other locations) PARK_ESCORT Parking escort SHUTTLEVAN Shuttle Vehicle RENTAL Rental car HOTEL_COURTESY Hotel transportation WALK Walk WALK_LOC, WALK_PRM, WALK_MIX Walk transit modes KNR_LOC, KNR_PRM, KNR_MIX KNR transit modes TNC_LOC, TNC_PRM, TNC_MIX TNC transit modes"},{"location":"outputs.html#assignment-model-trip-tables-assignment","title":"Assignment model trip tables (.\\assignment)","text":"
This directory contains trip tables from auto and transit assignments.
"},{"location":"outputs.html#demand-matrices","title":"Demand Matrices","text":"File Description autoairportTrips.(airport)_(period_(vot).omx Auto trip table for airport (CBX, SAN) by period (EA, AM, MD, PM, EV) and value of time (low, medium, high) autocrossborderTrips_(period)_(vot).omx Auto trip table for cross border model by period (EA, AM, MD, PM, EV) and value of time (low, medium, high) autoTrips_(period)_(vot).omx Auto trip table for resident model by period (EA, AM, MD, PM, EV) and value of time (low, medium, high) autovisitorTrips_(period)_(vot).omx Auto trip table for visitor model by period (EA, AM, MD, PM, EV) and value of time (low, medium, high) emptyAVTrips.omx Empty private autonomous vehicle trips householdAVTrips.csv All private autonomous vehicle trips TNCTrips.csv All TNC trips TNCVehicleTrips_(period).omx TNC vehicle trip table by period (EA, AM, MD, PM, EV) TranairportTrips.(airport)_(period).omx Transit trip tables for airport (CBX, SAN) by period (EA, AM, MD, PM, EV) TrancrossborderTrips_(period).omx Transit trip tables for cross-border model by period (EA, AM, MD, PM, EV) TranTrips_(period).omx Transit trip tables for resident model by period (EA, AM, MD, PM, EV) TranvisitorTrips_(period).omx Transit trip tables for visitor model by period (EA, AM, MD, PM, EV) TripMatrices.csv Disaggregate commercial vehicle trips"},{"location":"outputs.html#airport-model-auto-demand-matrices","title":"Airport Model auto demand matrices","text":"Table Name Description SR2_<> Shared Ride 2 for <> SR3_<> Shared Ride 3 for <> SOV_<> Drive Alone for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#airport-model-transit-demand-matrices","title":"Airport Model transit demand matrices","text":"Table Name Description WLK_SET_set1_<> PNR_SET_set1_<> KNR_SET_set1_<> TNC_SET_set1_<> WLK_SET_set2_<> PNR_SET_set2_<> KNR_SET_set2_<> TNC_SET_set2_<> WLK_SET_set3_<> PNR_SET_set3_<> KNR_SET_set3_<> TNC_SET_set3_<> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#airport-model-non-motorized-demand-matrices","title":"Airport Model non-motorized demand matrices","text":"Table Name Description WALK_<> Walk for <> BIKE_<> Bike for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#crossborder-model-auto-demand-matrices","title":"Crossborder Model auto demand matrices","text":"Table Name Description SR2_<> Shared Ride 2 for <> SR3_<> Shared Ride 3 for <> SOV_<> Drive Alone for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#crossborder-model-transit-demand-matrices","title":"Crossborder Model transit demand matrices","text":"Table Name Description WLK_SET_set1_<> PNR_SET_set1_<> KNR_SET_set1_<> TNC_SET_set1_<> WLK_SET_set2_<> PNR_SET_set2_<> KNR_SET_set2_<> TNC_SET_set2_<> WLK_SET_set3_<> PNR_SET_set3_<> KNR_SET_set3_<> TNC_SET_set3_<> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#crossborder-model-non-motorized-demand-matrices","title":"Crossborder Model non-motorized demand matrices","text":"Table Name Description WALK_<> Walk for <> BIKE_<> Bike for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#visitor-model-auto-demand-matrices","title":"Visitor Model auto demand matrices","text":"Table Name Description SR2_<> Shared Ride 2 for <> SR3_<> Shared Ride 3 for <> SOV_<> Drive Alone for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#visitor-model-transit-demand-matrices","title":"Visitor Model transit demand matrices","text":"Table Name Description WLK_SET_set1_<> PNR_SET_set1_<> KNR_SET_set1_<> TNC_SET_set1_<> WLK_SET_set2_<> PNR_SET_set2_<> KNR_SET_set2_<> TNC_SET_set2_<> WLK_SET_set3_<> PNR_SET_set3_<> KNR_SET_set3_<> TNC_SET_set3_<> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#visitor-model-non-motorized-demand-matrices","title":"Visitor Model non-motorized demand matrices","text":"Table Name Description WALK_<> Walk for <> BIKE_<> Bike for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#tnc-vehicle-trip-demand-table","title":"TNC Vehicle trip demand table","text":"Column Name Description trip_ID Trip ID vehicle_ID Vehicle ID originTaz Origin TAZ destinationTaz Destination TAZ originMgra Origin MGRA destinationMgra Destination MGRA totalPassengers Number of passengers in the vehicle startPeriod Trip starting period endPeriod Trip ending period pickupIdsAtOrigin Trip id of the pick-up at origin. CR-RAMP: \u2003\u2003Individual trips: \u2003\u2003\"I_\" + personId + \"_\" + purpAbb + \"_\" + tourid + \"_\" + inbound + \"_\" + stopid \u2003\u2003\u2003where purpAbb is the first 3 letters of the tour_purp field \u2003\u2003Joint trips: \u2003\u2003\"J_\" + hhid + \"_\" + purpAbb + \"_\" + tourid + \"_\" + inbound + \"_\" + stopid + \u201d_\u201d + i \u2003\u2003\u2003where i is a number ranging from 1 to the total number of participants. Visitor trips: \u2003\u2003partySize == 1: \"V_\" + tourid + \"_\" + stopid \u2003\u2003partySize > 1: \"V_\" + tourid + \"_\" + stopid + \u201d_\u201d + i \u2003\u2003\u2003where i is a number ranging from 1 to the total number of participants. Cross-border trips: \u2003\u2003partySize == 1: \"M_\" + tourid + \"_\" + stopid \u2003\u2003partySize > 1: \"M_\" + tourid + \"_\" + stopid + \u201d_\u201d + i \u2003\u2003\u2003where i is a number ranging from 1 to the total number of participants. CBX airport trips: \u2003\u2003partySize == 1: \"CBX_\" + tourid + \"_\" + stopid \u2003\u2003partySize>1: \"CBX_\" + tourid + \"_\" + stopid + \u201d_\u201d + i \u2003\u2003\u2003where i is a number ranging from 1 to the total number of participants. SAN airport trips: \u2003\u2003partySize == 1: \"SAN_\" + tourid + \"_\" + stopid \u2003\u2003partySize > 1: \"SAN_\" + tourid + \"_\" + stopid + \u201d_\u201d + i \u2003\u2003\u2003where i is a number ranging from 1 to the total number of participants. Internal-External trips: \u2003\u2003\"IE_\" + tourid + \"_\" + inbound dropoffIdsAtOrigin Trip id of the drop-off at origin. See pickupIdsAtOrigin for trip id of the trip. pickupIdsAtDestination Trip id of the pick-up at destination. See pickupIdsAtOrigin for trip id of the trip. dropoffIdsAtDestination Trip id of the drop-off at destination. See pickupIdsAtOrigin for trip id of the trip. originPurpose Trip origin purpose destinationPurpose Trip destination purpose"},{"location":"outputs.html#household-autonomous-vehicle-trip-data","title":"Household autonomous vehicle trip data","text":"Column Name Description hh_id Household id veh_id Vehicle id vehicleTrip_id Vehicle trip id orig_mgra Trip origin MGRA dest_gra Trip destination MGRA period Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM occupants Number of occupants in the vehicle originIsHome Is origin home 0 = No 1 = Yes destinationIsHome Is destination home 0 = No 1 = Yes originIsRemoteParking Is origin remote parking 0 = No 1 = Yes destinationIsRemoteParking Is destination remote parking 0 = No 1 = Yes parkingChoiceAtDestination Parking choice at destination: 0 = Not constrained to remote parking 1 = Park at destination 2 = Remote parking 3 = Park at home person_id Person id person_num Person number tour_id Tour id stop_id Stop id inbound Is trip inbound 1 = Yes 0 = No tour_purpose Tour purpose: Discretionary Eating Out Escort Home Maintenance School Shop University Visiting Work Work-Based orig_purpose Origin trip purpose: Discretionary Eating Out Escort Home Maintenance School Shop University Visiting Work Work-Based Work related dest_purpose Destination trip purpose: Discretionary Eating Out Escort Home Maintenance School Shop University Visiting Work Work-Based Work related trip_orig_mgra Trip origin MGRA trip_dest_mgra Trip destination MGRA stop_period Stop period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM periodsUntilNextTrip trip_mode Trip mode: 0 = Empty vehicle trip 1 = Drive Alone 2 = Shared Ride 2 3 = Shared Ride 3"},{"location":"outputs.html#tnc-vehicle-trip-matrix","title":"TNC vehicle trip matrix","text":"Table Name Description TNC_<>_0 TNC trips for <> with 0 passenger TNC_<>_1 TNC trips for <> with 1 passenger TNC_<>_2 TNC trips for <> with 2 passengers TNC_<>_3 TNC trips for <> with 3 or more passengers"},{"location":"outputs.html#empty-autonomous-vehicle-trips-data","title":"Empty Autonomous vehicle trips data","text":"Table Name Description EmptyAV_EA Empty AV trips for EA period EmptyAV_AM Empty AV trips for AM period EmptyAV_MD Empty AV trips for MD period EmptyAV_PM Empty AV trips for PM period EmptyAV_EV Empty AV trips for EV period"},{"location":"outputs.html#crossborder-model-outputs-crossborder","title":"Crossborder model outputs (.\\crossborder)","text":"
This directory contains outputs from the Crossborder model, which represents all travel made by Mexico residents in San Diego County.
File Description final_accessibility.csv Accessibility file for Crossborder Model (not used, created by default) [final_households.csv](#### Crossborder Model household file (final_households.csv)) Household file for Crossborder Model final_land_use.csv Land-use file for Crossborder Model [final_persons.csv](#### Crossborder Model person file (final_persons.csv)) Persons file for Crossborder Model [final_tours.csv](#### Crossborder Model tour file (final_tours.csv)) Tour file for Crossborder Model [final_trips.csv](#### Crossborder Model trip file (final_trips.csv)) Tour file for Crossborder Model model_metadata.yaml Model run meta data for use in Datalake storage and reporting nmCrossborderTrips_AM.omx Non-motorized trip table for Crossborder Model by period (EA, AM, MD, PM, EV) autoCrossborderTrips_AM.omx Auto trip table for Crossborder Model by period (EA, AM, MD, PM, EV) tranCrossborderTrips_AM.omx Transit trip table for Crossborder Model by period (EA, AM, MD, PM, EV) othrCrossborderTrips_AM.omx Other trip table for Crossborder Model by period (EA, AM, MD, PM, EV)"},{"location":"outputs.html#crossborder-model-household-file-final_householdscsv","title":"Crossborder Model household file (final_households.csv)","text":"Field Description sample_rate Sample Rate num_persons Number of persons in travel party origin Origin MGRA (Border crossing station) home_zone_id Home MGRA (Border crossing station) household_id Household ID poverty Poverty indicator utilized for social equity reports. Percentage value where value <= 2 (200% of the Federal Poverty Level) indicates household is classified under poverty."},{"location":"outputs.html#crossborder-model-person-file-final_personscsv","title":"Crossborder Model person file (final_persons.csv)","text":"Field Description household_id Household ID work_time_factor Travel time sensitivity factor for work tours non_work_time_factor Travel time sensitivity factor for non-work tours (Sampled in person preprocessor) origin Origin MGRA (Border crossing station) home_zone_id Home MGRA (Border crossing station) person_id Person ID"},{"location":"outputs.html#crossborder-model-tour-file-final_tourscsv","title":"Crossborder Model tour file (final_tours.csv)","text":"Field Description tour_id Tour ID pass_type Type of border crossing pass. String. \"no_pass\": Does not own a pass, \"sentri\": SENTRI pass, or \"ready\": READY pass tour_type Tour purpose. String. \"other\", \"school\", \"shop\", \"visit\", or \"work\" purpose_id Tour purpose ID. 0: work, 1: school, 2: shop, 3: visit, 4: other tour_category Tour category. String. Mandatory: Work or school, Non-Mandatory: Shop, visit, other number_of_participants Number of participants in tour household_id Household ID person_id Person ID start Half-hour time period of departure from tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. end Half-hour time period of arrival back at tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. duration Duration of the tour in number of half-hour periods, including all activity episodes and travel origin Tour origin (Border Crossing) MGRA destination Tour primary destination MGRA tour_od_logsum Tour origin-crossing-destination logsum poe_id Number of border crossing station tour_mode Tour mode mode_choice_logsum Tour mode choice logsum stop_frequency Number of stops on tour by direction. String. xout_yin where x is number of stops in the outbound direction and y is the number of stops in the inbound direction primary_purpose will drop"},{"location":"outputs.html#crossborder-model-trip-file-final_tripscsv","title":"Crossborder Model trip file (final_trips.csv)","text":"Field Description trip_id Trip ID person_id Person ID household_id Household ID primary_purpose Purpose at primary destination. String. \"other\", \"school\", \"shop\", \"visit\", or \"work\" trip_num Sequential number of trip on half-tour from 1 to 4 outbound TRUE if outbound, else FALSE trip_count number of trips per tour. Will drop destination Destination MGRA origin Origin MGRA tour_id Tour ID purpose Purpose at trip destination. String. \"other\", \"school\", \"shop\", \"visit\", or \"work\" depart Departure time period (1\u202648) trip_mode Trip mode (see trip mode table) trip_mode_choice_logsum Mode choice logsum for trip parking_cost Parking costs at trip origin and destination, calculated as one-half of the costs at each end, with subsidies considered. tnc_single_wait_time Wait time for single pay TNC tnc_shared_wait_time Wait time for shared\\pooled TNC taxi_wait_time Wait time for taxi cost_parking Cost of parking ($2023) cost_fare_drive Taxi/TNC fare (including Taxi/TNC cost of transit access/egress) ($2023) distance_walk Distance walked in miles (including access/egress walk distances of a transit mode) time_mm Micromobility time distance_mm Micromobility distance cost_fare_mm Micromobility cost ($2023) distance_bike Bike distance time_wait_drive Wait times for Taxi/TNC/NEV modes trip_period A string indicating the skim period for the trip (\u201cEA\u201d,\u201dAM\u201d,\u201dMD\u201d,\u201dPM\u2019,\u201dEV\u201d) tour_participants Number of joint tour participants if joint tour, else 1 distance_total Total distance traveled on a trip cost_operating_drive Auto operating cost ($2023) weight_trip Trip weight defined as the ratio of number of particpants on a trip to the assumed occupancy rate of a mode (SHARED2,3) weight_person_trip Person trip weight defined as the ratio of the number of participants on a trip to sample rate of the model run inbound TRUE if trip is in outbound direction, else FALSE time_drive Auto time distance_drive Auto distance cost_toll_drive Auto toll cost ($2023) time_transit_in_vehicle Transit in-vehicle time time_rapid_transit_in_vehicle Rapid transit in-vehicle time time_express_bus_transit_in_vehicle Express bus in-vehicle time time_local_bus_transit_in_vehicle Local bus in-vehicle time time_light_rail_transit_in_vehicle Light rail transit in-vehicle time time_commuter_rail_transit_in_vehicle Commuter rail in-vehicle time time_transit_initial_wait Transit initial-wait time cost_fare_transit Transit fare ($2023) transfers_transit Number of transfers time_bike Bike time time_walk Walk mode time cost_total Sum of all costs a trip might incur (auto operating, toll, transit fare) time_total Total travel time (including iIVT and access/egress and wait times for all modes) value_of_time_category_id Value of time bin. 1: Low, 2: Medium, 3: High origin_micro_prm_dist Distance from trip origin MGRA to closest premium transit stop by microtransit dest_micro_prm_dist Distance from trip destination MGRA to closest premium transit stop by microtransit microtransit_orig Distance from trip origin MGRA to closest local transit stop by microtransit microtransit_dest Distance from trip destination MGRA to closest local transit stop by microtransit microtransit_available TRUE if microtransit is available for trip, else FALSE nev_orig True if Neighborhood Electric Vehicle is available at origin nev_dest True if Neighborhood Electric Vehicle is available at destination nev_available TRUE if Neighborhood Electric Vehicle is available, else FALSE microtransit_access_available_out TRUE if microtransit is available from the origin, else FALSE nev_access_available_out TRUE if neighborhood electric vehicle is available from the origin, else FALSE microtransit_egress_available_out Availability of microtransit egress in the outbound direction nev_egress_available_out Availability of NEV egress in the outbound direction microtransit_access_available_in Availability of microtransit access in the inbound direction nev_access_available_in Availability of NEV egress in the inbound direction microtransit_egress_available_in Availability of microtransit egress in the inbound direction nev_egress_available_in Availability of microtransit egress in the outbound direction sample_rate Sample rate otaz Origin TAZ dtaz Destination TAZ"},{"location":"outputs.html#crossborder-model-tour-mode-definitions","title":"Crossborder Model Tour Mode Definitions","text":"Field Description DRIVEALONE Drive alone SHARED2 Shared ride with 2 participants SHARED3 Shared ride with 3+ participants WALK Walk"},{"location":"outputs.html#commercial-vehicle-model-cvm","title":"Commercial Vehicle Model (.\\cvm)","text":"
This directory contains intermediate files and final expected parking costs calculated from input parking supply data and walk distances between MGRAs.
File Description aggregated_street_data.csv Street length and intersections aggregated to MGRA level, used to estimate free on-street parking spaces cache (directory) Directory containing intermediate calculations for expected parking costs distances.csv MGRA-MGRA distances used for expected parking cost calculations districts.csv Calculated parking districts at MGRA level used for expected parking cost calculations final_parking_data.csv Expected hourly, daily, and monthly parking costs, total spaces, and parking district at the MGRA level for use in travel models plots Directory containing plots of the parking model results shapefiles Directory containing shapefiles for parking model calculations"},{"location":"outputs.html#resident-model-outputs-resident","title":"Resident model outputs (.\\resident)","text":"
This directory contains San Diego resident travel model outputs.
File Description cdap_joint_spec_(persons).csv Model specification file for coordinated daily activity pattern model joint tour alternative for (persons)-way interaction terms cdap_spec_(persons).csv Model specification file for coordinated daily activity pattern model for (persons)-way interaction terms. data_dict.csv Data dictionary for resident model, csv format data_dict.txt Data dictionary for resident model, text format final_accessibility.csv Resident model aggregate accessibility file final_disaggregate_accessibility.csv Resident model disaggregate accessibility file at MGRA level [final_households.csv](#### Resident Model household file (final_households.csv)) Resident model household file [final_joint_tour_participants.csv](#### Resident Model joint tour participants file (final_joint_tour_participants.csv)) Resident model joint tour participants file final_land_use.csv Resident model land-use file [final_persons.csv](#### Resident Model vehicle file (final_vehicles.csv)) Resident model persons file final_proto_disaggregate_accessibility.csv Resident model disaggregate accessibility file at person level [final_tours.csv](#### Resident Model tour file (final_tours.csv)) Resident model tour file [final_trips.csv](#### Resident Model trips file (final_trips.csv)) Resident model trip file [final_vehicles.csv](#### Resident Model vehicle table (final_vehicles.csv)) Resident model vehicle file log (directory) Directory for resident model logging output model_metadata.yaml Resident model Datalake metadata file autoTrips_[tod]_[vot].omx Residential Auto Trip Matrix for 5 time periods (tod = EA, AM, MD, PM, EV) and three value of time bins (vot = low, med, high) tranTrips_[tod].omx Residential Transit Trip Matrix for 5 time periods (tod = EA, AM, MD, PM, EV) nmotTrips_[tod].omx Residential Non-motorized Trip Matrix for 5 time periods (tod = EA, AM, MD, PM, EV) skim_usage.txt Skim usage file trace (directory) Directory for resident model trace output"},{"location":"outputs.html#resident-model-household-file-final_householdscsv","title":"Resident Model household file (final_households.csv)","text":"Field Description home_zone_id Household MGRA - same as mgra income Household income in dollars ($2023) hhsize Number of persons in household HHT Household dwelling unit type. 0: N/A (GQ/vacant), 1: Married couple household, 2: Other family household: Male householder no spouse present, 3: Other family household: Female householder no spouse present, 4: Nonfamily household: Male householder living alone, 5: Nonfamily household: Male householder: Not living alone, 6: Nonfamily household: Female householder: Living alone, 7: Nonfamily household: Female householder: Not living alone auto_ownership (Model output) Auto ownership num_workers Number of workers in household building_category Units in structure. 0: N/A (GQ), 1: Mobile home or trailer, 2: One-family house detached, 3: One-family house attached, 4: 2 Apartments, 5: 3-4 Apartments, 6: 5-9 Apartments, 7: 10-19 Apartments, 8: 20-49 Apartments, 9: 50 or more apartments, 10: Boat, RV, van, etc. unittype Household unit type. 0: Non-GQ Household, 1: GQ Household (used in Visualizer) sample_rate Sample rate for household income_in_thousands Household income in thousands of dollars ($2023) income_segment Household income segment (1-4) num_non_workers Number of non-workers in household num_drivers Number of persons age 16+ num_adults Number of persons age 18+ ebike_owner TRUE if household owns an e-bike, else FALSE (output from e-bike owership simulation) av_ownership TRUE if household owns an autonomous vehicle, else FALSE (output from AV Ownership Model) workplace_location_accessibility Work location choice logsum (output from Disaggregate Accessibility Model) shopping_accessibility Shopping primary destination choice logsum (output from Disaggregate Accessibility Model) othdiscr_accessibility Other Discretionary primary destination choice logsum (output from Disaggregate Accessibility Model) numAVowned Number of autonomous vehicles owned by household (output from Vehicle Type Choice Model) transponder_ownership TRUE if household owns a transponder, else FALSE (output from Transponder Ownership Model) has_joint_tour 1 if household has at least one fully joint tour, else false (output from Coordinated Daily Activity Pattern Model) num_under16_not_at_school Number of persons age less than 16 who do not attend school (output from Coordinated Daily Activity Pattern Model) num_travel_active Number of persons in household who have an active (type M or N) travel pattern (output from Coordinated Daily Activity Pattern Model) num_travel_active_adults Number of adults in household who have an active (type M or N) travel pattern (output from Coordinated Daily Activity Pattern Model) num_travel_active_preschoolers Number of preschool children in household who have an active (type M or N) travel pattern (output from Coordinated Daily Activity Pattern Model) num_travel_active_children Number of children in household who have an active (type M or N) travel pattern (output from Coordinated Daily Activity Pattern Model) num_travel_active_non_preschoolers Number of non-preschoolers household in who have an active (type M or N) travel pattern (output from Coordinated Daily Activity Pattern Model) participates_in_jtf_model TRUE if household has a joint tour frequency model, else FALSE (output from Coordinated Daily Activity Pattern Model) school_escorting_outbound Alternative number for school escort model in the outbound direction (initial output from School Escort Model) school_escorting_inbound Alternative number for school escort model in the inbound direction (output from School Escort Model) school_escorting_outbound_cond Alternative number for school escort model in the outbound direction (final output from School Escort Model) auPkRetail Auto peak access to retail employment from household TAZ (aggregate accessibility output) auPkTotal Auto peak access to total employment from household TAZ (aggregate accessibility output) auOpRetail Auto offpeak access to retail employment from household TAZ (aggregate accessibility output) auOpTotal Auto offpeak access to total employment from household TAZ (aggregate accessibility output) trPkRetail Transit peak access to retail employment from household TAZ (aggregate accessibility output) trPkTotal Transit peak access to total employment from household TAZ (aggregate accessibility output) trPkHH Transit peak access to total employment from household (aggregate accessibility output) trOpRetail Transit offpeak access to retail employment from household TAZ (aggregate accessibility output) trOpTotal Transit offpeak access to total employment from household TAZ (aggregate accessibility output) nmRetail Walk access to retail employment from household TAZ (aggregate accessibility output) nmTotal Walk access to total employment from household TAZ (aggregate accessibility output) microtransit Microtransit access time in household MGRA nev Neighborhood electric vehicle access time in household MGRA mgra Household MGRA - same as home_zone_id TAZ Household TAZ micro_dist_local_bus Distance to closest local bus stop from household MGRA by microtransit, if available. 999999 if not available. micro_dist_premium_transit Distance to closest premium transit stop from household MGRA by microtransit, if available. 999999 if not available. joint_tour_frequency_composition Joint tour frequency and composition model choice (output from Joint Tour Frequency\\Composition Model) num_hh_joint_tours Number of fully joint tours at the household level (0, 1 or 2) (output from Coordinated Daily Activity Pattern Model and Joint Tour Frequency\\Composition Models) household_id Household ID poverty Poverty indicator utilized for social equity reports. Percentage value where value <= 2 (200% of the Federal Poverty Level) indicates household is classified under poverty.
Resident Model person file (final_persons.csv)
Field Description household_id Household ID age Person age in years PNUM Person number in household (1\u2026n where n is number of persons in household) sex 1: Male, 2: Female pemploy Employment status of person. 1: Employed Full-Time, 2: Employed Part-Time, 3: Unemployed or Not in Labor Force, 4: Less than 16 Years Old pstudent Student status of person. 1: Pre K-12, 2: College Undergrad+Grad and Prof. School, 3: Not Attending School ptype Person type 1: Full-time worker 2: Part-time worker 3: College\\University Student 4: Non-Working Adult 5: Retired 6: Driving-age student 7: Non-driving age student 8: Pre-school\\Age <=5 educ Educational attainment. 1: No schooling completed, 9: High school graduate, 13: Bacehlor's degree soc2 Two-digit Standard Occupational Classification (SOC) codes (https://www.bls.gov/oes/current/oes_stru.htm) is_student Person is a K12 or college student school_segment School location choice model's segment a student belongs to (preschool, grade school, high school, university) is_worker Person is a full-time or part-time worker is_internal_worker TRUE if worker works internal to region, else FALSE (output from Internal-External Worker Identification Model) is_external_worker TRUE if worker works external to region, else FALSE (output from Internal-External Worker Identification Model) home_zone_id Household MGRA time_factor_work Travel time sensitivity factor for work tours time_factor_nonwork Travel time sensitivity factor for non-work tours (Sampled in person preprocessor) naics_code Two-digit NAICS code (https://www.census.gov/naics/) occupation Occupation String work_from_home TRUE if worker and works from home, else FALSE (output from Work From Home Model) is_out_of_home_worker TRUE if worker has a usual out of home work location, else FALSE (output from Work From Home Model) external_workplace_zone_id MGRA number of external workplace if external worker, else -1 (output from External Workplace Location Choice Model) external_workplace_location_logsum Location choice logsum for external workplace location choice model (output from External Workplace Location Choice Model) external_workplace_modechoice_logsum Mode choice logsum for mode choice from external workplace location choice model (output from External Workplace Location Choice Model) school_zone_id MGRA number of school location, else -9 (output from School Location Choice Model) school_location_logsum Location choice logsum for school location choice model, else -9 (output from School Location Choice Model) school_modechoice_logsum Mode choice logsum for mode choice from school location choice model, else -9 (output from School Location Choice Model) distance_to_school Distance to school if student, else -9 (output from School Location Choice Model) roundtrip_auto_time_to_school Round trip offpeak auto time to school, else -9 (output from School Location Choice Model) workplace_zone_id MGRA number of internal work location, else -9 (output from Internal Work Location Choice Model) workplace_location_logsum Location choice logsum for work location choice model, else -9 (output from Internal Work Location Choice Model) workplace_modechoice_logsum Mode choice logsum for mode choice from work location choice model, else -9 (output from Internal Work Location Choice Model) distance_to_work Distance to work if internal worker with work location, else -9 (output from Internal Work Location Choice Model) work_zone_area_type Area type of work zone for worker if internal worker with work location, else -9 (output from Internal Work Location Choice Model) auto_time_home_to_work Peak auto time from home to work if internal worker with work location, else -9 (output from Internal Work Location Choice Model) roundtrip_auto_time_to_work Round trip auto travel time to and from work work_auto_savings Travel time savings as a result of using auto vs. walk-transit mode exp_daily_work Expected daily cost of parking at work if internal worker with work location, else -9 (output from Internal Work Location Choice Model) non_toll_time_work Time from home to work for path without I-15, if worker with internal workplace, else -9 toll_time_work Time from home to work for path with I-15, if worker with internal workplace, else -9 toll_dist_work Travel distance for work using a tolled route toll_cost_work Toll cost for going to work toll_travel_time_savings_work Work travel time savings for using tolled vs. non-tolled routes transit_pass_subsidy 1 if person has subsidized transit from their employer or school, else 0 (Output from Transit Subsidy Model) transit_pass_ownership 1 if person owns a transit pass, else 0 (Output from Transit Pass Ownership Model) free_parking_at_work TRUE if person has free parking at work, else FALSE (Output from Free Parking Model) telecommute_frequency Telecommute frequency if worker who does not work from hom, else null (Output from Telecommute Frequency Model) String \"No_Telecommute\", \"1_day_week\", \"2_3_days_week\", \"4_days_week\" cdap_activity Coordinated daily activity pattern type (Output from Coordinated Daily Activity Pattern Model) String \"M\": Mandatory pattern, \"N\": Non-mandatory pattern, \"H\": Home or out of region pattern travel_active TRUE if activity pattern is \"M\" or \"N\", else FALSE (Output from Coordinated Daily Activity Pattern Model) num_joint_tours Total number of fully joint tours (Output from Fully Joint Tour Participation Model) non_mandatory_tour_frequency Non-Mandatory Tour Frequency Model Choice (Output from Non-Mandatory Tour Frequency Chopice Model) num_non_mand Total number of non-mandatory tours (Output from School Escort Model, Non-Mandatory Tour Frequency Model, and At-Work Subtour Model) num_escort_tours Total number of escorting tours (Output from School Escort Model and Non-Mandatory Tour Frequency Model) num_eatout_tours Total number of eating out tours (Output from Non-Mandatory Tour Frequency Model) num_shop_tours Total number of shopping tours (Output from Non-Mandatory Tour Frequency Model) num_maint_tours Total number of other maintenance tours (Output from Non-Mandatory Tour Frequency Model) num_discr_tours Total number of discretionary tours (Output from Non-Mandatory Tour Frequency Model) num_social_tours Total number of social\\visiting tours (Output from Non-Mandatory Tour Frequency Model) num_add_shop_maint_tours Total number of additional shopping and maintenance tours (Output from Non-Mandatory Tour Frequency Extension Model) num_add_soc_discr_tours Total number of additional social\\visiting and other discretionary tours (Output from Non-Mandatory Tour Frequency Extension Model) person_id Person ID miltary 1 if serves in the military, else 0 grade School grade of person: 0 = N/A (not attending school), 2 = K to grade 8, 5 = Grade 9 to grade 12, 6 = College undergraduate weeks Weeks worked during past 12 months 0: N/A (less than 16 years old/did not work during the past 12 .months) 1: 50 to 52 weeks worked during past 12 months 2: 48 to 49 weeks worked during past 12 months 3: 40 to 47 weeks worked during past 12 months 4: 27 to 39 weeks worked during past 12 month 5: 14 to 26 weeks worked during past 12 months 6: 13 weeks or less worked during past 12 months hours Usual hours worked per week past 12 months
0: .N/A (less than 16 years old/did not work during the past .12 months), 1..98 .1 to 98 usual hours, 99 .99 or more usual hours race Recoded detailed race code 1: .White alone, 2: Black or African American alone, 3: American Indian alone, 4: Alaska Native alone, 5: American Indian and Alaska Native tribes specified; or .American Indian or Alaska Native, not specified and no other races, 6: Asian alone, 7: Native Hawaiian and Other Pacific Islander alone, 8: Some Other Race alone, 9: Two or More Races hispanic Hispanic flag: 1: Non-Hispanic, 2: Hispanic
Resident Model vehicle file (final_vehicles.csv)
Field Description vehicle_id Vehicle ID household_id Household ID vehicle_num Vehicle number in household from 1\u2026n where n is total vehicles owned by household vehicle_type String bodytype_age_fueltype auto_operating_cost Auto operating cost for vehicle ($2023 cents/mile) Range Range if electric vehicle, else 0 MPG Miles per gallen for vehicle vehicle_year Year of vehicle vehicle_category String, Body type (Car, Motorcycle, Pickup, SUV, Van. Autonomous vehicles have _AV extension on body type) num_occupants Number of occupants in the vehicle fuel_type String. BEV: Battery electric vehicle, Diesel, Gas, Hybrid: Gas\\Electric non plug-in vehicle, PEV: Plug-in hybrid electric vehicle"},{"location":"outputs.html#resident-model-joint-tour-participants-file-final_joint_tour_participantscsv","title":"Resident Model joint tour participants file (final_joint_tour_participants.csv)","text":"Field Description participant_id Participant ID tour_id Tour ID household_id Household ID person_id Person ID participant_num Sequent number of participant 1\u2026n where n is total number of participants in joint tour"},{"location":"outputs.html#resident-model-tour-file-final_tourscsv","title":"Resident Model tour file (final_tours.csv)","text":"Field Description tour_id Tour ID person_id Person ID tour_type Purpose string of the primary activity on the tour: For home-based tours, the purposes are: \u201cwork\u201d, \u201cschool\u201d, \u201cescort\u201d, \u201cshopping\u201d, \u201cothmaint\u201d, \u201ceatout\u201d, \u201csocial\u201d, and \u201cothdiscr\u201d. For work-based subtours, the purposes are \u201cbusiness\u201d, \u201ceat\u201d, and \u201cmaint\u201d. tour_type_count The total number of tours within the tour_type tour_type_num The sequential number of the tour within the tour_category. In other words if a person has 3 tours; 1 work tour and 2 non-mandatory tours, the tour_type_num would be 1 for the work tour, 1 for the first non-mandatory tour and 2 for the second non-mandatory tour. tour_num Sequential tour ID number for a person tour_count Total number of tours per person tour_category The category string of the primary activity on the tour. \u201cmandatory\u201d, \u201cjoint\u201d, \u201cnon_mandatory\u201d, \u201catwork\u201d number_of_participants Number of participants on the tour for fully joint tours, else 1 destination MGRA number of primary destination origin MGRA number of tour origin household_id Household ID start Half-hour time period of departure from tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. end Half-hour time period of arrival back at tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. duration Duration of the tour in number of half-hour periods, including all activity episodes and travel school_esc_outbound For school tours where the child is being escorted according to the school pickup/dropoff model, this string field indicates the type of escorting in the outbound direction: \u201cpure_escort\u201d or \u201crideshare\u201d school_esc_inbound For school tours where the child is being escorted according to the school pickup/dropoff model, this string field indicates the type of escorting in the inbound direction: \u201cpure_escort\u201d or \u201crideshare\u201d num_escortees Number of children being escorted on this tour (max of outbound and inbound direction) tdd Tour departure and duration. Index of the tour departure and durarion alterntive configs composition Composition of tour if joint \u201cadults\u201d, \u201cchildren\u201d is_external_tour TRUE if primary destination activity is external to region, else FALSE is_internal_tour Whether tour is internal destination_logsum Logsum from tour destination choice model vehicle_occup_1 Tour vehicle with occupancy of 1 vehicle_occup_2 Tour vehicle with occupancy of 2 vehicle_occup_3_5 Tour vehicle with occupancy of 3+ tour_mode Tour mode string mode_choice_logsum Logsum from tour mode choice model selected_vehicle Selected vehicle from vehicle type choice model; a string field consisting of [Body type][age][fuel type] and an optional extension \u201c_AV\u201d if the vehicle is an autonomous vehicle atwork_subtour_frequency At-work subtour frequency choice model result; a string field with the following values: \u201cno_subtours\u201d, \u201cbusiness1\u201d, \u201cbusiness2\u201d, \u201ceat\u201d, \u201ceat_business\u201d, \u201cmaint\u201d, or blank for non-work tours. parent_tour_id Parent tour ID if this is a work-based subtour, else 0 stop_frequency Stop frequency choice model result; a string value of the form [0\u2026n]out_[0\u2026n]in where the first number is the number of outbound stops and the second number is the number of inbound stops primary_purpose Recoding of tour_type where all atwork subtours are identified as \u201catwork\u201d regardless of destination purpose"},{"location":"outputs.html#resident-model-trip-file-final_tripscsv","title":"Resident Model trip file (final_trips.csv)","text":"Field Description trip_id Trip ID person_id Person ID household_id Household ID primary_purpose Primary purpose of tour (see tour table) trip_num Sequential number of trip by direction (1\u2026n where n is maximum trips on half-tour, e.g. max stops + 1) outbound TRUE if trip is in the outbound direction, else FALSE destination MGRA of trip destination origin MGRA of trip origin tour_id Tour ID escort_participants Space delimited string field listing person IDs of other children escorted on this trip, else null school_escort_direction String field indicating whether child is being dropped off at school (\u201coutbound\u201d) or picked up from school (\u201cinbound\u201d). \u201cnull\u201d if not a child being picked up or dropped off. purpose Purpose at destination destination_logsum Logsum from trip destination choice model. -9 if destination is tour origin or primary destination. depart Departure time period (1\u202648) trip_mode Trip mode string mode_choice_logsum Logsum from trip mode choice model vot Value of time for trip in dollars per hour ($2023) owns_transponder True if household owns transponder. Same as ownTrp totalWaitSingleTNC Wait time for single pay TNC totalWaitSharedTNC Wait time for shared\\pooled TNC s2_time_skims HOV2 travel time s2_dist_skims HOV3 travel distance s2_cost_skims HOV2 travel toll cost cost_parking Parking costs at trip origin and destination, calculated as one-half of the costs at each end, with subsidies considered. cost_fare_drive Taxi/TNC fare for any trip or trip portion taken on these modes distance_walk Distance walked on a trip (including access/egress for transit modes) time_mm Micromobility time distance_mm Micromobility distance cost_fare_mm Micromobility cost ($2023) distance_bike Bike distance time_wait_drive Waiting time for a TNC/ Taxi modes parking_zone MGRA from parking location choice model at destination, else -1 trip_period A string indicating the skim period for the trip (\u201cEA\u201d,\u201dAM\u201d,\u201dMD\u201d,\u201dPM\u2019,\u201dEV\u201d) tour_participants Number of joint tour participants if joint tour, else 1 distance_total Trip distance in miles cost_operating_drive Auto operating cost ($2023) weight_trip Trip weight defined as the ratio of number of particpants on a trip to the assumed occupancy rate of a mode (SHARED2,3) weight_person_trip Person trip weigth defined as the ratio of the number of particpants on a trip to sample rate of the model run inbound TRUE if trip is in the inbound direction, else FALSE time_drive Auto time distance_drive Auto distance cost_toll_drive Auto toll cost ($2023) time_transit_in_vehicle Transit in-vehicle time time_rapid_transit_in_vehicle Rapid transit in-vehicle time time_express_bus_transit_in_vehicle Express bus in-vehicle time time_local_bus_transit_in_vehicle Local bus in-vehicle time time_light_rail_transit_in_vehicle Light rail transit in-vehicle time time_commuter_rail_transit_in_vehicle Commuter rail in-vehicle time time_transit_initial_wait Transit initial-wait time cost_fare_transit Transit fare before subsidy ($2023) transfers_transit Number of transfers time_bike Bike time time_walk Walk mode time cost_total total cost of a trip (sum of auto operating, toll, transit fare) time_total Total time (sum of drive, bike, walk, initial transit wait, transit time, transit transfer)) time_transit_wait Total transit wait time (initial, transfer, NEV wait, waiting for school bus) value_of_time_category_id Value of time bin. 1: Low, 2: Medium, 3: High origin_micro_prm_dist Distance from trip origin MGRA to closest premium transit stop by microtransit dest_micro_prm_dist Distance from trip destination MGRA to closest premium transit stop by microtransit microtransit_orig Distance from trip origin MGRA to closest local transit stop by microtransit microtransit_dest Distance from trip destination MGRA to closest local transit stop by microtransit microtransit_available TRUE if microtransit is available for trip, else FALSE nev_orig Availability of Neighborhood Electric vehicle at origin nev_dest Availability of Neighborhood Electric vehicle at destination nev_available TRUE if neighborhood electric vehicle is available, else FALSE microtransit_access_available_out TRUE if microtransit is available from the origin, else FALSE nev_access_available_out TRUE if neighborhood electric vehicle is available from the origin, else FALSE microtransit_egress_available_out Availability of microtransit egress in the outbound direction nev_egress_available_out Availability of NEV egress in the outbound direction microtransit_access_available_in Availability of microtransit access in the inbound direction nev_access_available_in Availability of NEV egress in the inbound direction microtransit_egress_available_in Availability of microtransit egress in the inbound direction nev_egress_available_in Availability of microtransit egress in the outbound direction trip_veh_body Body type of vehicle used for trip, else \u201cnull\u201d trip_veh_age Age of vehicle used for trip, else \u201cnull\u201d trip_veh_fueltype Fuel type of vehicle used for trip, else \u201cnull\u201d origin_purpose Purpose at origin sample_rate Sample rate origin_parking_zone MGRA from parking location choice model at trip origin, else -1 otaz Origin TAZ dtaz Destination TAZ"},{"location":"outputs.html#resident-model-tour-mode-definitions","title":"Resident Model tour mode definitions","text":"Field Description DRIVEALONE Drive alone SHARED2 Shared ride with 2 participants SHARED3 Shared ride with 3+ participants WALK Walk BIKE Bike WALK_LOC Local transit with walk access/egress mode WALK_PRM Premium transit with walk access/egress mode WALK_MIX Mix (local with premium transfers) transit with walk access/egress mode PNR_LOC Local transit with Park&ride access or egress mode PNR_PRM Premium transit with Park&ride access or egress mode PNR_MIX Mix (local with premium transfers) transit with Park&ride access or egress mode KNR_LOC Local transit with Kiss&ride access or egress mode KNR_PRM Premium transit with Kiss&ride access or egress mode KNR_MIX Mix (local with premium transfers) transit with Kiss&ride access or egress mode TNC_LOC Local transit with TNC access or egress mode TNC_PRM Premium transit with TNC access or egress mode TNC_MIX Mix (local with premium transfers) transit with TNC access or egress mode TAXI Taxi TNC_SINGLE Private TNC ride TNC_SHARED Shared TNC ride SCH_BUS School bus EBIKE E-bike ESCOOTER E-scooter"},{"location":"outputs.html#resident-model-auto-demand-matrices","title":"Resident Model auto demand matrices","text":"Table Name Description SOVNOTRPDR_<> Drive Alone Non-Transponder for <> SOVTRPDR_<> Drive Alone Transponder for <> SR2NOTRPDR_<> Shared Ride 2 Non-Transponder for <> SR2TRPDR_<> Shared Ride 2 Transponder for <> SR3NOTRPDR_<> Shared Ride 3 Non-Transponder for <> SR3TRPDR_<> Shared Ride 3 Transponder for <>"},{"location":"outputs.html#resident-model-transit-demand-matrices","title":"Resident Model transit demand matrices","text":"Table Name Description <transit_class>_GENCOST__<> Total generalized cost which includes perception factors from assignment <transit_class>_FIRSTWAIT__<> actual wait time at initial boarding point <transit_class>_XFERWAIT__<> actual wait time at all transfer boarding points <transit_class>_TOTALWAIT__<> total actual wait time <transit_class>_FARE__<> fare paid <transit_class>_XFERS__<> number of transfers <transit_class>_ACCWALK__<> access actual walk time prior to initial boarding <transit_class>_EGRWALK__<> egress actual walk time after final alighting <transit_class>_TOTALWALK__<> total actual walk time <transit_class>_TOTALIVTT__<> Total actual in-vehicle travel time <transit_class>_DWELLTIME__<> Total dwell time at stops <transit_class>_BUSIVTT__<> actual in-vehicle travel time on local bus mode <transit_class>_LRTIVTT__<> actual in-vehicle travel time on LRT mode <transit_class> _CMRIVTT__<> actual in-vehicle travel time on commuter rail mode <transit_class> _EXPIVTT__<> actual in-vehicle travel time on express bus mode <transit_class>_LTDEXPIVTT__<> actual in-vehicle travel time on premium bus mode <transit_class>_BRTIVTT__<> actual in-vehicle travel time on BRT mode *time period = EA, AM, MD, PM, EV transit_class = BUS, ALLPEN, PREM"},{"location":"outputs.html#resident-model-non-motorized-demand-matrices","title":"Resident Model non-motorized demand matrices","text":"Table Name Description WALK_<> Walk for <> BIKE_<> Bike for <>"},{"location":"outputs.html#visitor-model-outputs-visitor","title":"Visitor model outputs (.\\visitor)","text":"
This directory contains outputs from the overnight visitor model.
File Description [final_households.csv](#### Visitor Model household file (final_households.csv)) Visitor model household file final_land_use.csv Visitor model land-use file [final_persons.csv](#### Visitor Model person file (final_persons.csv)) Visitor model person file [final_tours.csv](#### Visitor Model tour file (final_tours.csv)) Visitor model tour file [final_trips.csv](#### Visitor Model trip file (final_trips.csv)) Visitor model trip file model_metadata.yaml Visitor model Datalake metadata file nmotVisitortrips_(period).omx Visitor model non-motorized trips by period (EA, AM, MD, PM, EV) autoVisitortrips_(period).omx Visitor model auto trips by period (EA, AM, MD, PM, EV) transVisitortrips_(period).omx Visitor model transit trips by period (EA, AM, MD, PM, EV)"},{"location":"outputs.html#visitor-model-household-file-final_householdscsv","title":"Visitor Model household file (final_households.csv)","text":"Field Description home_zone_id Home MGRA sample_rate Sample rate household_id Household ID poverty Poverty indicator utilized for social equity reports. Percentage value where value <= 2 (200% of the Federal Poverty Level) indicates household is classified under poverty."},{"location":"outputs.html#visitor-model-person-file-final_personscsv","title":"Visitor Model person file (final_persons.csv)","text":"Field Description household_id Household ID home_zone_id Home MGRA person_id Person ID"},{"location":"outputs.html#visitor-model-tour-file-final_tourscsv","title":"Visitor Model tour file (final_tours.csv)","text":"Field Description tour_id Tour ID tour_type Type of tour. String. \"dining\", \"recreation\", or \"work\" purpose_id Type of tour. 0: work, 1: \"dining, 2: \"recreation\" visitor_travel_type Visitor purpose. String. \"business\" or \"personal\" tour_category Tour category. All tour categories in the visitor model are \"non-mandatory\" number_of_participants Number of participants on tour auto_available Auto availability indicator 0: not available, 1: available income Income 0 - 4 origin Tour origin MGRA tour_num Sequential number of tour 1 to n where n is total number of tours tour_count Number of tours per person household_id Household ID person_id Person ID start Half-hour time period of departure from tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. end Half-hour time period of arrival back at tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. duration Duration of the tour in number of half-hour periods, including all activity episodes and travel destination Tour primary destination MGRA destination_logsum Tour destination choice logsum tour_mode Tour mode mode_choice_logsum Tour mode choice logsum stop_frequency Number of stops on tour by direction. String. xout_yin where x is number of stops in the outbound direction and y is the number of stops in the inbound direction primary_purpose Primary purpose of a tour. String (recreation, dining, work)"},{"location":"outputs.html#visitor-model-trip-file-final_tripscsv","title":"Visitor Model trip file ((final_trips.csv)","text":"Field Description trip_id Trip ID person_id Person ID household_id Household ID primary_purpose Purpose at primary destination of tour. String. \"dining\", \"recreation\", or \"work\" trip_num Sequential number of trip on half-tour from 1 to 4 outbound TRUE if outbound, else FALSE trip_count Number of trips in a tour destination Destination MGRA origin Origin MGRA tour_id Tour ID purpose Destination purpose. String. \"dining\", \"recreation\", or \"work\" destination_logsum Destination choice logsum depart Departure time period (1\u202648) trip_mode Trip mode (see trip mode table) trip_mode_choice_logsum Mode choice logsum for trip vot_da will drop vot_s2 will drop vot_s3 will drop parking_cost Parking costs at trip origin and destination, calculated as one-half of the costs at each end, with subsidies considered. tnc_single_wait_time Wait time for single pay TNC tnc_shared_wait_time Wait time for shared\\pooled TNC taxi_wait_time Wait time for taxi cost_parking Cost of parking ($2023) cost_fare_drive Taxi/TNC fare for any trip or trip portion taken on these modes distance_walk Distance walked on a trip (including access/egress for transit modes) time_mm Micromobility time distance_mm Micromobility distance cost_fare_mm Micromobility cost ($2023) distance_bike Bike distance time_wait_drive Ridehail/Taxi wait times for a trip trip_period A string indicating the skim period for the trip (\u201cEA\u201d,\u201dAM\u201d,\u201dMD\u201d,\u201dPM\u2019,\u201dEV\u201d) tour_participants Number of tour participants distance_total Trip distance cost_operating_drive Auto operating cost ($2023) weight_trip Trip weight defined as the ratio of number of particpants on a trip to the assumed occupancy rate of a mode (SHARED2,3) weight_person_trip Person trip weigth defined as the ratio of the number of particpants on a trip to sample rate of the model run vot Value of time in dollars per hour ($2023) inbound TRUE if trip is in outbound direction, else FALSE time_drive Auto time distance_drive Auto distance cost_toll_drive Auto toll cost ($2023) time_transit_in_vehicle Transit in-vehicle time time_rapid_transit_in_vehicle Rapid transit in-vehicle time time_express_bus_transit_in_vehicle Express bus in-vehicle time time_local_bus_transit_in_vehicle Local bus in-vehicle time time_light_rail_transit_in_vehicle Light rail transit in-vehicle time time_commuter_rail_transit_in_vehicle Commuter rail in-vehicle time time_transit_initial_wait Transit initial-wait time cost_fare_transit Transit fare ($2023) transfers_transit Number of transfers time_bike Bike time time_walk Walk mode time cost_total total cost of a trip (sum of auto operating, toll, transit fare) time_total Total travel time (including iIVT and access/egress and wait times for all modes) value_of_time_category_id Value of time bin. 1: Low, 2: Medium, 3: High origin_micro_prm_dist Distance from trip origin MGRA to closest premium transit stop by microtransit dest_micro_prm_dist Distance from trip destination MGRA to closest premium transit stop by microtransit microtransit_orig Distance from trip origin MGRA to closest local transit stop by microtransit microtransit_dest Distance from trip destination MGRA to closest local transit stop by microtransit microtransit_available TRUE if microtransit is available for trip, else FALSE nev_orig True if Neghoborhood Electric Vehicle is available at origin nev_dest True if Neghoborhood Electric Vehicle is available at destination nev_available TRUE if neighborhood electric vehicle is available, else FALSE microtransit_access_available_out TRUE if microtransit is available from the origin, else FALSE nev_access_available_out TRUE if neighborhood electric vehicle is available from the origin, else FALSE microtransit_egress_available_out Availability of microtransit egress in the outbound direction nev_egress_available_out Availability of NEV egress in the outbound direction microtransit_access_available_in Availability of microtransit access in the inbound direction nev_access_available_in Availability of NEV egress in the inbound direction microtransit_egress_available_in Availability of microtransit egress in the inbound direction nev_egress_available_in Availability of microtransit egress in the outbound direction sample_rate Sample rate otaz Origin TAZ dtaz Destination TAZ"},{"location":"outputs.html#visitor-models-tour-mode-choice-definitions","title":"Visitor model\u2019s tour mode choice definitions","text":"Field Description DRIVEALONE Drive alone SHARED2 Shared ride with 2 participants SHARED3 Shared ride with 3+ participants WALK Walk BIKE Bike WALK_LOC Local transit with walk access/egress mode WALK_PRM Premium transit with walk access/egress mode WALK_MIX Mix (local with premium transfers) transit with walk access/egress mode TAXI Taxi TNC_SINGLE Private TNC ride TNC_SHARED Shared TNC ride"},{"location":"outputs.html#trip-mode-definitions","title":"Trip Mode Definitions","text":"Field Description DRIVEALONE Drive alone SHARED2 Shared ride with 2 participants SHARED3 Shared ride with 3+ participants WALK Walk BIKE Bike WALK_LOC Local transit with walk access/egress mode WALK_PRM Premium transit with walk access/egress mode WALK_MIX Mix (local with premium transfers) transit with walk access/egress mode PNR_LOC Local transit with Park&ride access or egress mode PNR_PRM Premium transit with Park&ride access or egress mode PNR_MIX Mix (local with premium transfers) transit with Park&ride access or egress mode KNR_LOC Local transit with Kiss&ride access or egress mode KNR_PRM Premium transit with Kiss&ride access or egress mode KNR_MIX Mix (local with premium transfers) transit with Kiss&ride access or egress mode TNC_LOC Local transit with TNC access or egress mode TNC_PRM Premium transit with TNC access or egress mode TNC_MIX Mix (local with premium transfers) transit with TNC access or egress mode TAXI Taxi TNC_SINGLE Private TNC ride TNC_SHARED Shared TNC ride SCH_BUS School bus EBIKE E-bike ESCOOTER E-scooter"},{"location":"release-notes.html","title":"Release Notes","text":""},{"location":"release-notes.html#version-1510-september-4-2024","title":"Version 15.1.0 (September 4, 2024)","text":"
As mentioned in the notes for Version 15.0.2, several improvements to the Commercial Vehicle Model (CVM) were made, largely due to SANDAG staff realizing that the survey used to estimate the CVM had likely overestimated the amount of commercial vehicle travel that was made on a given day in the region. New weights were estimated, and then the CVM was recalibrated to match these new weights. After doing this, it was found that modeled highway volumes were lower than observed counts, so some further adjustments were made to get them back up. Some components of the resident model were recalibrated to better match the survey, a new database started being used, a bug in the transit network was fixed, and other miscelaneous improvements were made.
PR 167, PR 172, & PR 173: Recalibration of the Commercial Vehicle Model to reflect newly-calculated Commercial Vehicle Survey weights.
PR 182, PR 186, PR 194, & PR 195: Miscellaneous calibration, particularly to counter the decrease in freeway travel caused by the CVM update.
PR 176 & PR 177: A new database was created for Version 15.1.0 and necessary scripts were modified to point to the new database. Go here to see detailed changes in the database and here for changes in the reporting tool.
PR 184, PR 186, & PR 193: The number of EV chargers each year were updated using numbers given by planning, and recalibration was done to ensure that the EV ownership rates for no build scenarios matched the targets.
PR 178, PR 188, & PR 189: In previous releases the crossborder model had its own constants.yaml file, which contained a lot of the same values in the common constants.yaml file, meaning that both files would need to be edited. The crossborder model now instead reads from the common constants.yaml file.
PR 179, PR 181, PR 190, PR 191, & PR 192: Miscellaneous updates to the scenario creation GUI and scenario build process.
PR 168, PR 170, PR 171, PR 175, & PR 185: A bug in the transit network where connectors were being encoded with the wrong units was fixed. This resulted in a sharp increase in the number of transit boardings so some additional calibration was needed to get them back down to observed levels.
PR 174 & PR 180: Miscellaneous file cleanup and restoration.
Since the release of version 15.0.1 more updates were made, particularly regarding the new commercial vehicle model. More updates to the CVM will be forthcoming due to ongoing revision based on new data sources.
For use in the 2025 Region Plan (RP), SANDAG has developed a new activity-based travel model, known as ABM3. The biggest change from SANDAG\u2019s previous ABM, ABM2+, is the transition from the Java-based CT-RAMP modeling platform to the open-source Python-based ActivitySim platform that has been developed by a consortium of public agencies (of which SANDAG is a founding member) for the past decade. ABM3 will be the first ActivitySim model to be used in production for planning purposes. While most of the model components from ABM3 were translated from ABM2+, there were several notable enhancements that were made, which are described below. Further, several models were either re-estimated and/or recalibrated to match 2022 data.
SANDAG/activitysim v15.0.0: This is based on a fork of the ActivitySim consortium repo and customized code in BayDAG_estimation branch.
SANDAG/activitysim-cvm v15.0.0: This specific version of ActivitySim is required to run the new Commercial Vehicle Model (CVM) in ABM3. It is based on a fork of the ActivitySim repo under CamSys and customized code in time-settings branch.
ABM PR 68: The base year was updated from 2016 to 2022, allowing the behavioral models to reflect changes in travel behavior derived from the onset of the COVID-19 pandemic.
ABM PR 65: The zone system was transitioned from a 3-zone system (TAZ, MAZ, TAP) to a 2-zone system (TAZ, MAZ).
ABM PR 66: E-bike and E-scooter were added as travel modes.
ABM PR 64: A new commercial vehicle model (CVM) was implemented using ActivitySim, that includes firm synthesis, commercial vehicle tours, and TNC services such as food delivery.
ABM PR 133: Network editing was changed from the ArcInfo-based TCOVED to TNED, a new network editing system.
ABM PR 104: Flexible fleet services were added for full trips along with first-mile access to and last-mile egress from fixed-route transit.
ABM PR 9: The airport model has been updated with new access options, including the proposed automated people-mover.
ABM PR 53: The cross-border model was updated, including a new wait time model.
Asim PR 635: A new disaggregate accessibility calculation step was introduced to improve the precision of accessibilities used by modeling components.
ABM PR 82: Transit assignment for different time periods is now run in parallel.
ABM Configs: The auto ownership model has been moved before work and school location.
Asim PR 613: The shadow pricing algorithm was updated to constrain mandatory tour locations to availability instead of traditional shadow pricing.
ABM PR 86: Model results are automatically written to a database stored in the cloud via Microsoft Azure Services.
"},{"location":"release-notes.html#new-resident-model-components","title":"New Resident Model Components","text":"
Autonomous Vehicle Ownership
External Worker Identification
External Workplace Location
Transit Pass Subsidy
Transit Pass Ownership
Vehicle Type Choice
Telecommute Frequency
External Joint Tour Identification
External Joint Tour Location
External Nonmandatory Tour Identification
External Nonmandatory Tour Destination
Vehicle Allocation
"},{"location":"running.html","title":"Installing and Running ABM3","text":"
This page describes how to install and run ABM3, including hardware and software requirements. In general, a powerful server is required to run the model. The main software required for the model includes EMME, Python, and Java. EMME is a commercial transportation modeling platform that must be purchased separately and requires a computer with a Windows operating system. Python is an open-source cross-platform programming language that is currently one of the most popular programming languages. Python is the core language of ActivitySim. Java is an open-source programming language required for certain bespoke non-ActivitySim model components.
ABM3 runs on a Microsoft Windows workstation or server, with the minimum and recommended system specification as follows:
Minimum specification:
Operating System: 64-bit Windows 7, 64-bit Windows 8 (8.1), 64-bit Windows 10, 64-bit Windows Server 2019
Processor: 24-core CPU processor
Memory: 1 TB RAM
Disk space: 500 GB
Recommended specification:
Operating System: 64-bit Windows 10
Processor: Intel CPU Xeon Gold / AMD CPU Threadripper Pro (24+ cores)
Memory: 1 TB RAM
Disk space: 1000 GB
In general, a higher CPU core count and RAM will result in faster run times as long as ActivitySim is configured to utilize the additional processors and RAM.
Note that the model is unlikely to run on servers that have less than 1 TB of RAM, unless chunking is set to active and in explicit mode (requires an upcoming version of ActivitySim 1.3)
Three software applications, EMME, Python package manager Anaconda, and Java should be installed on the computer that will be used to run the model.
The ABM3 model system is an integrated model that is controlled by and primarily runs in the EMME transportation planning software platform. EMME is used for network assignment and creating transportation skims, and the model\u2019s Graphical User Interface (GUI). The software also provides functionality for viewing and editing highway and transit network files and viewing of matrix files. The Bentley Connect Edition software (the license manager for EMME) will need to be logged in and activated prior to running the model.
A Python package manager is software that creates an environment for an instance of Python. ActivitySim and related Python processes in the model are executed in an environment that is setup with a specific version of Python and specific library versions. This ensures that changes outside of the Python environment will not cause errors or change model results, and additionally ensure that the specific version of Python and specific libraries needed by the model do not cause errors or changes to other Python software installations on the server. The libraries needed by ActivitySim extend the base functionality of Python. Note that Anaconda requires a paid subscription for agencies larger than 200 users. To install Anaconda, follow the instructions here.
Java is required in order to create bicycle logsums, run the taxi/TNC routing model, and run the intra-household autonomous vehicle routing model. The model has been tested against Java version 8 (e.g. 1.8) but should run in later versions as well.
"},{"location":"running.html#installing-abm3-model","title":"Installing ABM3 Model","text":""},{"location":"running.html#setting-up-the-python-environments","title":"Setting up the Python environments","text":"
As noted above, User needs to install Anaconda on the machine they are working on, if it is not already installed. The following step is creating a specific environment to run ActivitySim. The environment is a configuration of Python that is for ActivitySim - this environment allows ActivitySim to use specific software libraries without interfering with the server\u2019s installed version of Python (if one exists, it is not required) and keeps other Python installations from interfering with ActivitySim.
To run ABM3, user needs to install two different python environments, one in Python 3, which will be used by all ActivitySim-based models, and one in Python 2, which is required as long as the EMME version in use still depends on Python 2, and is used to convert the omx rip tables out of the ActivitySim models. To set up these environments, use the following instruction from within the Anaconda 3 PowerShell Prompt for Python 3 and Anaconda 2 PowerShell Prompt for Python 2.
To set up the Python 3 environment, first, change directories using cd /d to the environment folder under the ActivtySim source code directory. As of June 2024, this directory may be cloned from the BayDAG_estimation branch located on the SANDAG\u2019s forked version of ActivitySim here. The environment folder in this directory contains a number of yaml files that may be used to install the environment. User may use the following command to install the AcitvitySim environment along with SANDAG\u2019s version of AcitivtySim under the asim_baydag name.
After installing the environment, do a quick test of it by activating it, using:
conda activate asim_baydag
To set up the Python 2 environment, user simply needs to install the openmatrix package in the base environment. To do so, first open the Anaconda 2 terminal and use the following command to install the openmatrix package:
Java version 1.81 needs to be installed on the server. SANDAG servers usually have this version of Java already installed on them.
"},{"location":"running.html#creating-a-scenario-folder","title":"Creating a scenario folder","text":"
Follow the steps below to create a model scenario folder using SANDAG\u2019s tool:
Check for an open server using the DOS tool mascot-abm.
Log into server
Using the show hidden icons arrow on the taskbar, click the INRO Software Manager to check if EMME license is operating. If it\u2019s not, log into license server on Mustang by typing in the Host box: mustang.sandag.org:5170 (automatic)
Using the server\u2019s Windows Explorer, navigate to T:\\ABM\\release\\ABM\\version_14_2_2\\dist
Double click on createStudyAndScenario.exe and click Run.
On pop-up, enter fields in Create an ABM scenario. Make sure to enter the correct ABM version, EMME version, year, and know your Scenario Folder (where you will run the model from; typically study area/abm_runs) and your Network Folder (input networks). Browse to an existing folder or copy (ctrl + c) and paste (ctrl + v) paths to minimize chance of typos. After fields are entered, click Create.
A DOS window will open and necessary files will be copied. If you have a server with a valid license, it will pause for about 30-45 seconds on init emme folder. You will get a pop-up window that says you have successfully created the scenario. Click Quit. Exit the GUI by clicking on Quit.
Go to your newly created abm_run folder and double check on input files/date stamps to verify they match expected date stamps. Make any modifications to input files (i.e. parametersByYears.csv for adjusting auto operating costs, filesByYears.csv for specifying year-specific files, or mgra_based_inputXXX.csv file for adjusting parking costs)
To open the EMME application from the created scenario directory, user needs to go to the emme_project folder, and open the start_emme_with_virtualenv.bat file. This opens up the EMME application, where the application prompts the choice of a scenario. It is recommended to select the main highway scenario (Scen. 100) to start off the model run, although other scenarios may be selected as well.
Following this step, user should open the EMME Modeler by clicking on the gold square sign at the top left of the screen.
EMME Modeler icon
The EMME Modeler opens to the EMME Standard Toolbox, but needs to be switched to the SANDAG toolbox by selecting it from the bottom-left of the screen. From this toolbox, open the Master Run tool.
Opening SANDAG Toolbox and Master run
Opening the Master Run tool allows the user to run all or part of the model, and set a number of settings such as sample size.
Master Run tool
The Master run tool operates the SANDAG travel demand model. To operate the model, configure the inputs by providing Scenario ID, Scenario title and Emmebank title, and keeping Number of Processors to default. Select main ABM directory will automatically be set to the current project directory and does not require change.
Main ABM directory: directory which contains the ABM scenario data, including this project. The default is the parent directory of the current Emme project.
Scenario ID: Scenario ID for the base imported network data. The result scenarios are indexed in the next five scenarios by time period.
Scenario title: title to use for the scenario.
Emmebank title: title to use for the Emmebank (Emme database)
Number of processors: the number of processors to use for traffic and transit assignments and skims, aggregate demand models (where required) and other parallelized procedures in Emme. Default is Max available \u2013 1.
Properties (conf/sandag_abm.properties)
On opening the Master run tool the sandag_abm.properties file is read and the values cached and the inputs below are pre-set. When the Run button is clicked this file is written out with the values specified. Any manual changes to the file in-between opening the tool and clicking the Run button are overwritten.
Sample rate by iteration: three comma-separated values for the ActivitySim sample rates for each iteration
Start from iteration: start iteration for the model run
Skip steps: optional checkboxes to skip model steps.
Select link: add select link analyses for traffic.
By expanding the Run model \u2013 skip steps drop down, the user can make any custom changes. Usually the defaults should be sufficient although if you are using a new bike network, you should uncheck the Skip bike logsums and check the Skip copy of bikelogsum.
Run model tool
Following this setup, you can click Run to start the model run. We recommend occasionally checking the model run status to make sure the run is going smoothly. When the model run finishes successfully, the Master Run tool will show a model run successful message in green at the top of the tool window.
If the run is unsuccessful (there will be an error prompt from Emme), check Emme logbook and log files (under \u201clogfiles\u201d) for clues to where it stopped.
As the model runs, a full runtime trace of the model steps, inputs and reports is recorded in the Modeller Logbook. As each step completes, it will record an entry in the Logbook along with reports and other results. The Logbook can be opened from the Clock-like icon in the upper right of the Modeller window. This icon can also be found in the toolbar in the Emme Desktop. If a Modeller tool is running, a window will pop-up over the Emme Desktop which includes a Show Logbook button (this window can be closed to use Desktop worksheets and tables while any tool is running). Click on the Refresh button to update the logbook view with the latest status.
Modeller logbook
The Logbook provides a real time, automated documentation of the model execution. The overall structure of the model is represented at the top level, with the occurrence, sequence and repetition of the steps involved in the model process. Nested Logbook entries may be collapsed or expanded to see detail. For the Emme assignment procedures, interactive charts are recorded. The statistical summaries of the impedance matrices are recorded for each time period following the assignment. These summary tables provide an easy way to check for skims with obvious outlier values.
"},{"location":"application/applying.html","title":"Applying the model","text":"
This page contains information needed to apply the model.
One of the policies that SANDAG planners would like to test for the 2025 Regional Plan is providing rebates for low- and middle-income households to purchase electric vehicles. One of the variables in the vehicle type choice model is the new purchase price for a vehicle of a given age, body type, and fuel type. The way the EV rebate is implemented in ABM3 is by deducting the appropriate rebate value for plugin and battery vehicles if a household meets the criteria (based on percentage of the federal poverty level). To configure the rebate values and poverty level thresholds, new constants were added to the common/constants.yaml configuration file. The constants fit into the policy as follows:
The of the five big moves defined in SANDAG\u2019s 2021 regional plan was Flexible Fleets, which involves on-demand transit services. The initial concept of the 2025 Regional Plan involves rapidly expanding these services, with many new services planned to be in operation by 2035. For this reason, it is important that these services be modeled by ABM3. There are two flavors of flexible fleets that were incorporated into ABM3, Neighborhood Electric Vehicles (NEV) and microtransit. A table contrasting these services is shown below.
Characteristic NEV Microtransit Vehicle Size Smaller Larger Service Area Smaller Larger Operating Speed Slower Faster"},{"location":"application/flexible-fleets.html#incorporation-into-abm3","title":"Incorporation into ABM3","text":"
Rather than creating new modes for flexible fleet services, microtransit and NEV were incorporated into existing modes. How this was done was dependent on whether the trip was a full flexible fleet trip, first-mile access to fixed-route transit, or last-mile egress from fixed-route transit. A table explaining how each of these trip types was incorporated into ABM3 is shown below. Further, a heirarchy of services is enforced. ActivitySim first checks if NEV is available (based on a new land use attribute), and if it is, it\u2019s assumed that NEV is used. If not, ActivitySim checks if microtransit is available (based on a corresponding land use attribute), and if it is, it\u2019s assumed that microtransit is used. If neither are available, ActivitySim looks at the other services that are already available.
*For trips on the return leg of a tour the access and egress attributes are swapped
Full microtransit trip First-mile access to fixed-route transit Last-mile egress from fixed-route transit What models allow for this type of trip? Resident, Visitor, Crossborder Resident Resident, Visitor, Crossborder Which mode is used? TNC Shared TNC to transit All transit modes How is the flexible fleet travel time factored into the trip? The travel time is the full travel time of the trip The travel time is added to the transit access time and a transfer is added The travel time is added to the transit egress time and a transfer is added if the destination is further from the nearest transit stop than a user would be willing to walk (that distance is configurable) How is the flexible fleet cost factored into the trip? The cost is the full cost of the trip It is assumed that flexible fleet services are free when used to access fixed-route transit It is assumed that flexible fleet services are free when egressing from fixed-route transit"},{"location":"application/flexible-fleets.html#new-attributes","title":"New Attributes","text":"
Several new attributes were added to allow the user to configure how flexible fleet services are operated. These are all defined in the common constants.yaml file. Each attribute is defined as follow:
Attribute Definition Default value Speed Assumed operating speed in miles per hour MT: 30, NEV: 17 Cost Cost of using service in US Cents 125 for both WaitTime Assumed time passengers wait to wait to use service in minutes 12 for both MaxDist Maximum distance in miles that the service can be used MT: 4.5, NEV: 3 DiversionConstant Additional travel time to divert for servicing other passengers 6 for both DiversionFactor Time multiplier accounting for diversion to service other passengers 1.25 for both StartPeriod Time period to start service (not yet implemented) 9 for both EndPeriod Time period to end service (not yet implemented) MT: 32, NEV: 38 maxWalkIfMTAccessAvailable Maximum disatance someone is willing to walk at the destination end if flexible fleet services are available (same for microtransit and NEV) 1.0"},{"location":"application/flexible-fleets.html#travel-time-calculation","title":"Travel Time Calculation","text":""},{"location":"application/flexible-fleets.html#direct-time","title":"Direct Time","text":"
The flexible fleet travel time calculation is a two-step process. The first step is to calculate the time that it would take to travel from the origin to the destination* directly without any diversion to pick up or drop off any passengers. This is done by taking the maximum of the time implied by the operating speed and the congested travel time:
\\(d = \\textnormal{Distance from origin to destination (taken from distance skim)}\\)
\\(t_{\\textnormal{congested}} = \\textnormal{Congested travel time from origin to destination (taken from Shared Ride 3 time skim)}\\)
*When used to access fixed-route transit, the destination is the nearest transit stop to the trip origin. When used to egress from fixed-route transit, the origin is the nearest transit stop to the trip destination.
The second step of the travel time calculation was to account for diversion to pick up other passengers. These were based on guidelines used in a NEV pilot. The formula to calculated the total flexible fleet travel time is as follows:
ABM3 uses a python module as the scenario manager. The job of this scenario manager is updating the parameters used throughout the model to match a specific scenario\u2019s definition and needs. A number of these parameters including auto operating cost, taxi and TNC fare, micromobility cost, and AV ownership penetration are usually assumed to change by forecast year or scenario.
Manually changing these parameters requires the model user to know where each parameter is located, and individually changing them according to the scenario forecast values. A scenario manager, therefore, can be a convenient and efficient tool to automate this process.
The ABM3 Scenario Manager reads in a CSV input file (located under input/parametersByYears.csv) containing the parameter values for each scenario, and updates the associated parameters in the ActivitySim config files. A snapshot of this input parameter CSV file is shown below, where each row is associated with a specific scenario year/name. The parameter names used here can either be identical to the parameter names used in ActivitySim, or different. In case the parameter names are different, a separate file is used to map the parameters names between the input CSV and ActivitySim config files.
The scenario manager is run as part of the model setup in the Master Run tool before any ActivitySim model is run (usually only in the first iteration of the run). Model user can choose to run or skip this step, although it is highly recommended to run with each run to ensure correct parameters.
The ABM3 model system is primarily based on the ActivitySim platform; ActivitySim is used to model resident travel, cross-border travel, overnight visitor travel, airport ground access travel, and commercial vehicle travel including light, medium, and heavy commercial vehicles. Aggregate models are used to model external-internal travel (from external stations other than the U.S./Mexico border crossing) and through travel. The model system relies on EMME software for network processing, skimming, and assignment. Models are mostly implemented in Python, and some models are implemented in [Java] (https://www.java.com/en/).
The overall design of the model is shown in the figure below.
The system starts by performing initial input processing in EMME. This includes building transport networks and scenarios for skimming and assignment. An initial set of skims are created based on input trip tables (e.g. warm start). Then disaggregate choice models in ActivitySIm are run, including the resident model, the crossborder travel model, two airport ground access models, the overnight visitor model, and the commercial vehicle model. Next auxiliary models are run; the taxi/TNC routing model and the autonomous vehicle intra-household allocation model are run in Java. Aggregate external-internal and through travel models are run in Python. After all models are run, trip tables are built from the result and assigned to transport networks. A check is made to determine whether the model has reached convergence (currently this is set to three feedback iterations). If convergence is reached, outputs are processed for export to the SANDAG Datalake for reporting summaries. If not, speeds from assignment are averaged using method of successive averages, and skims are rebuilt for the next iteration. The model system is then re-run with the updated skims.
ActivitySim is used to represent all internal travel and internal-external made by residents of the SANDAG region (modeled area). The decision-makers in the model system include both persons and households. These decision-makers are created (synthesized) for each simulation year and land-use scenario, based on Census data and forecasted distributions of households and persons by key socio-economic categories. A similar but simplified method is used to generate disaggregate populations for cross-border, airport ground access, and overnight visitor models. The decision-makers are used in the subsequent discrete-choice models in a microsimulation framework where a single alternative is selected from a list of available alternatives according to a probability distribution. The probability distribution is generated from a logit model which considers the attributes of the decision-maker and the attributes of the various alternatives. The application paradigm is referred to as Monte Carlo simulation, since a random number draw is used to select an alternative from the probability distribution. The decision-making unit is an important element of model estimation and implementation and is explicitly identified for each model specified in the following sections.
A key advantage of using the micro-simulation approach is that there are essentially no computational constraints on the number of explanatory variables that can be included in a model specification. However, even with this flexibility, the model system will include some segmentation of decision-makers. Segmentation is a useful tool to both structure models (for example, each person type segment could have their own model for certain choices) and to characterize person roles within a household. Segments can be created for persons as well as households.
There are two airport ground access models - one for San Diego International Airport and one for the Crossborder Express terminal which provides access to Tijuana International Airport from the United States. Both models use the same structure and software code, though the parameters that control the total number of airport travel parties, off-airport destination, mode, arrival and departure times, and other characteristics, vary for each airport according to survey and airport-specific data. The airport ground access model simulates trips to and from the airport for residents, visitors, and external travelers. These trips are generated by arriving or departing passengers and are modeled as tours within the ActivitySim framework. A post processing script also generates trips to serve passengers who require a pickup or dropoff at the airport. For example, a passenger who is picked up at the airport generates two trips; one trip to the airport by the driver to pick up the air passenger(s), and another trip from the airport with the driver and the air passenger(s). It is important to note that, to work within the ActivitySim framework, the airport trips must be modeled as tours, rather than being generated directly as in the previous model. These tours are assigned an origin at the airport MGRA. During the stop frequency step of ActivitySim, a trip is assigned to the appropriate leg of the tour (either to or from the airport) while the opposite leg is not assigned any trips (referred to as the \u2018dummy leg\u2019). Passengers who are leaving on a departing flight and traveling to the airport are considered \u201cinbound,\u201d while arriving passengers are considered \u201coutbound\u201d.
The overall design of the model is shown in the figure below.
Tour Enumeration: A list of airport travel parties is generated from input enplanements and transferring passenger rates, as well as distributions that describe the share of travel parties by purpose (business versus personal), household income, and party size.
Tour Level Models
2.1 Tour Scheduling Probabilistic: The tour scheduling model uses a probabilistic draw of the scheduling distribution. This model assigns start and end times to the tour. This is important because it will also serve as the schedule model for the final airport trips. In ActivitySim, trips are scheduled based on the tour schedule. If there is only one trip per leg on the tour (such as our case here) the trip is assigned the tour start/end time.
2.2 Tour Destination Choice: The destination choice model chooses the non-airport end of the airport trips. Each tour is set with an origin at the airport MGRA. The tour destination model of ActivitySim is used to choose the non-airport end of the trip. The utility equation includes the travel distance, and the destination size terms. ActivitySim destination choice framework requires a mode choice log sum. A dummy tour mode choice log sum was created which generates a value of zero for every destination using the \u2018tour_mode_choice.csv\u2019 and \u2018tour_mode_choice.yml\u2019 file. This is a work around to prevent ActivitySim from crashing and not having to include the tour mode choice log sum in the destination choice model.
2.3 Stop Frequency Choice: The stop frequency model is where the trip table is first created. The pre-processor tags each tour with a direction of \u2018inbound\u2019 or \u2018outbound\u2019 according to whether the tour is a departing or arriving passenger. For the Airport Ground Access model, inbound tours are tagged with zero outbound trips and -1 inbound trips (and the opposite is true for outbound tours: -1 outbound trips and 0 inbound trips). The 0 signifies that no intermediate stops are made; this leg of the tour will only have one trip. The -1 signifies that no trip is made at all on that leg. Using the -1 allows us to create \u2018half-tours\u2019 where only one leg of the tour is recorded as a trip. 3. Trip Level Models
3.1 Trip Departure Choice: The trip scheduling model assigns depart times for each trip on a tour. ActivitySim requires trip scheduling probabilities; however, these are not used in this implementation since there is only one trip on any given tour leg. This means the trips will be assigned the tour scheduling times which were determined in the tour scheduling model. The trip scheduling probabilities file is just a dummy file.
3.2 Trip Mode Choice: Each trip is assigned a trip mode; in the Airport Ground Access Model, trip mode refers to the airport arrival mode which simultaneously predicts the arrival mode and the location which the passenger uses to access that model. The arrival modes are shown in the table below. The trip mode choice yaml file contains detailed variables associated with each trip mode. For example, each parking location is given an MGRA location, a walk time, a wait-time, and a cost. If a parking location MGRA is set to -999 it is assumed to be unavailable and will not be in the choice set. The pre-processor in this step stores all values of skims from the trip origin to each of the access modes destinations along with any associated costs. Costs include parking fees per day, access fees, fares, and rental car charges. Employees are not fed into the trip mode choice model. Instead, if a transit share is specified in the employee park file, that percentage of employees will be assigned \u2018Walk Premium\u2019 mode in the pre-processor. Otherwise, employees are all assigned \u2018Walk\u2019 mode from the employee parking lot to the terminal.
3.3 Airport Returns: Airport trips where the party is dropped of curbside or parked and escorted are assumed to also have the driver make a return trip to the non-airport location. This procedure is done as a post-processing step after mode choice and before trip tables are written out. An \u2018airport_returns.yml\u2019 file takes a user setting to determine which trip modes will include a return trip. These trips records are flagged and duplicated. The duplicated trips swap the origin and destination of the original trip and are assigned a unique trip id. These trips are tagged with \u2018trip_num =2\u2019 so they are easily sorted in any additional processing (such as for writing trip matrices).
3.4 Write trip matrices: The write trip matrices step converts the trip lists into vehicle trip matrices. The matrices are segmented by trip mode and value of time bins. The vehicle trip modes in the matrices include SOV, HOV2, HOV3+, Taxi, and TNC-single. Value of time segmentation is either low, medium, or high bins based on the thresholds set in the model settings.
The major tour modes are shown below:
flowchart TD\n subgraph four\n direction LR\n E[Ride-Hail] --> E1[Taxi]\n end\n subgraph three\n direction LR\n D[Transit] --> D1[Walk Access]\n %% D --> D2[PNR Access]\n D --> D3[KNR Access]\n D --> D4[TNC Access]\n\n D1 --> D11[Local Only]\n D1 --> D12[Premium Only]\n D1 --> D13[Mixed]\n\n D3 --> D31[Local Only]\n D3 --> D32[Premium Only]\n D3 --> D33[Mixed]\n\n D4 --> D41[Local Only]\n D4 --> D42[Premium Only]\n D4 --> D43[Mixed]\n end\n subgraph two\n direction LR\n C[Active] --> C1[Walk]\n end\n\n subgraph one\n direction LR\n B[Auto] --> B1[Drive-alone];\n B --> B2[Shared 2];\n B --> B3[Shared 3+];\n end\n A[Tour Mode] --> one;\n A --> two;\n A --> three;\n A --> four;\n\n classDef group1 fill:#f75f5f,stroke:#333,stroke-width:4px,font-size:26px,font-weight:bold;wrap\n classDef group2 fill:#ffd966,stroke:#333,stroke-width:2px,font-size:20px;wrap\n classDef group3 fill:#bdabf0,stroke:#333,stroke-width:2px,font-size:18px;wrap\n\n classDef hiddenTitle color:transparent;\n class one,two,three,four hiddenTitle;\n\n class A group1;\n class B,C,D,E group2;\n class B1,B2,B3,C1,C2,C3,C4,D1,D2,D3,D4,E1,E2,E3,D11,D12,D13,D21,D22,D23,D31,D32,D33,D41,D42,D43 group3;\n
"},{"location":"design/demand/airport.html#airport-ground-access-model-trip-arrival-modes","title":"Airport Ground Access Model Trip Arrival Modes","text":"Arrival Mode Description Park Location 1 Party parks personal vehicle at parking location 1. Park Location 2 Party parks personal vehicle at parking location 2. Park Location 3 Party parks personal vehicle at parking location 3. Park Location 4 Party parks personal vehicle at parking location 4. Park Location 5 Party parks personal vehicle at parking location 5. Curb Location 1 Party is dropped off or picked up by another driver at curbside location 1. Curb Location 2 Party is dropped off or picked up by another driver at curbside location 2. Curb Location 3 Party is dropped off or picked up by another driver at curbside location 3. Curb Location 4 Party is dropped off or picked up by another driver at curbside location 4. Curb Location 5 Party is dropped off or picked up by another driver at curbside location 5 Park and Escort Party is driven in personal vehicle, parks on-site at the airport and is escorted to/from airport. Rental Car Party arrives/departs by rental car. Shuttle Van Party takes shuttle van. Hotel Courtesy Party takes hotel courtesy transportation. Ridehail Location 1 Party arrives\\departs using ridehail at ridehail location 1 Ridehail Location 2 Party arrives\\departs using ridehail at ridehail location 2 Taxi Location 1 Party arrives\\departs using taxi at taxi location 1 Taxi Location 2 Party arrives\\departs using taxi at taxi location 2 Walk Local Party arrives\\departs using walk-local bus Walk Premium Party arrives\\departs using walk-premium transit Walk Mix Party arrives\\departs using walk-local plus premium transit KNR Local Party arrives\\departs using KNR-local bus KNR Premium Party arrives\\departs using KNR-premium transit KNR Mix Party arrives\\departs using KNR-local plus premium transit TNC Local Party arrives\\departs using TNC-local bus TNC Premium Party arrives\\departs using TNC-premium transit TNC Mix Party arrives\\departs using TNC-local plus premium transit Walk Party arrives\\departs using walk
For more information on the Air Ground Access Travel Model see technical documentation.
The Cross-Border Travel Model predicts travel made by residents of Mexico within San Diego County. It predicts the border crossing point of entry as well as all trips made within the county. The model is limited to simulating travel made by Mexican residents who return to Mexico within the simulation day. Cross-border travel not captured by the Cross-Border Model includes:
Residents of San Diego County who travel to/from Mexico. This travel is represented by the resident travel model.
Residents of Mexico (including U.S. citizens) who travel into San Diego County and who do not return to Mexico at the end of the day. This travel is represented in the overnight visitor travel model.
Travel between points of entry through San Diego County to other U.S. destinations. This travel is represented by the external-external travel model.
Commercial vehicle travel to/from points of entry. This travel is represented by the commercial vehicle model.
The overall design of the model is shown in the figure below.
"},{"location":"design/demand/crossborder.html#crossborder-model-purpose-definitions","title":"Crossborder Model Purpose Definitions","text":"
There are five activity purposes in the cross-border travel demand model: * Work: Any activity involving work for pay. * School: Pre-k school, K-12, college/university, or trade school. * Shop: Shopping at retail, wholesale, etc. * Visit: Visiting friends or family * Other: A broad category including eating out, medical appointments, recreational activities, etc.
Note that home activities are not listed, since we do not model activities south of the border.
"},{"location":"design/demand/crossborder.html#crossborder-model-mode-definitions","title":"Crossborder Model Mode Definitions","text":"
The major tour modes are shown below:
flowchart TD\n subgraph three\n direction LR\n D[Transit] --> D1[Walk Access]\n\n D1 --> D11[Local Only]\n D1 --> D12[Premium Only]\n D1 --> D13[Mixed]\n end\n subgraph two\n direction LR\n C[Active] --> C1[Walk]\n end\n\n subgraph one\n direction LR\n B[Auto] --> B1[Drive-alone];\n B --> B2[Shared 2];\n B --> B3[Shared 3+];\n end\n A[Tour Mode\\Border Crossing Mode] --> one;\n A --> two;\n A --> three;\n\n classDef group1 fill:#f75f5f,stroke:#333,stroke-width:4px,font-size:26px,font-weight:bold;wrap\n classDef group2 fill:#ffd966,stroke:#333,stroke-width:2px,font-size:20px;wrap\n classDef group3 fill:#bdabf0,stroke:#333,stroke-width:2px,font-size:18px;wrap\n\n classDef hiddenTitle color:transparent;\n class one,two,three hiddenTitle;\n\n class A group1;\n class B,C,D,E group2;\n class B1,B2,B3,C1,C2,C3,C4,D1,D11,D12,D13 group3;\n
The model has the following mode types at the trip level: * Drive-alone: Single occupant private vehicle * Shared 2: A private vehicle with exactly two passengers * Shared 3+: A private vehicle with three or more passengers * Walk: Walk mode * Bike: Bike mode * Walk-transit: Walk access to transit. There are three sub-types of transit: Local only, premium only, local + premium (which includes both local and premium services in the transit path) * Taxi: Door-to-door taxi trip * Single-pay TNC: Door-to-door TNC trip with a single payer (e.g. UberX) * Shared-pay TNC: Stop-to-stop TNC trip with potentially multiple payers (e.g. UberPool)
We also model tour mode, which is the mode used to cross the border. These modes include drive-alone, shared 2, shared 3+ and walk. We assume that anyone crossing by bus or taxi is similar to walk, since they do not have access to a personal vehicle for the rest of their travel in San Diego County.
We also classify border crossings by lane type: general purpose, SENTRI, and Ready. We assume that the use of these lanes is related to the border crossing party; we attribute each party with SENTRI or Ready availability. The proportion of total border crossing parties with access to SENTRI and Ready lanes are based on observed survey data, pooled across all stations. This data is used to simulate the availability of the lane to the travel party. Each lane crossing type is related to the wait time that the travel party experiences at each border crossing station by mode.
Below is a general description of the model structure.
Tour Enumeration: A list of person-tours is created by first cross-multiplying the input total person tours with the share of tours by pass type, then expanding tours by pass type to tours by pass type and purpose.
Tour Level Models 2.1 Time-of-day Choice: Each person-tour is assigned an outbound and return half-hour period.
2.2 Primary Destination and Station Choice: Each border crossing person-tour chooses a primary destination MGRA and border crossing station.
2.3 Border Crossing Mode Choice: Each person-tour chooses a border crossing tour mode.
Wait Time Model
3.1. Wait time model: Calculate wait time based on demand at each POE from model 2.2
3.2. Convergence check: If max iterations reached (currently 3), goto Stop and Trip level models, else goto Model 2.2 3. Stop and Trip Level Models 4.1 Stop Frequency Choice: Each person-tour is assigned number of stops by half-tour (outbound, return).
4.2 Stop Purpose Choice: Each stop is assigned a stop purpose (consistent with the tour purposes).
4.3 Trip Departure Choice: Each trip is assigned a half-hourly time period.
4.4 Stop Location Choice: Each stop chooses an MGRA location.
4.5 Trip Mode Choice: Each trip is assigned a trip mode.
4.6 Trip Assignments: Trips are assigned to networks, along with resident and other special market trip tables, and skims are created for the next iteration of the model.
For more information on the Crossborder Travel Model see technical documentation.
The resident model structure is based on the Coordinated Travel Regional Activity-based Modeling Platform (CT-RAMP). The figure below shows the resident model structure. In order to understand the flow chart, some definitions are required. These are described in more detail below.
Tour: A sequence of trips that start and end at an anchor location. In ActivitySim, anchors are home or work.
Primary destination: The \u201cmain\u201d activity of the tour; this activity determines the tour purpose. It also divides the tour into two \u201clegs\u201d; the sequence of trips from the anchor location to the primary destination is the outbound leg, and the sequence of trips from the primary destination back to the anchor location is the inbound or return leg.
Mandatory activity: Work or school
Non-mandatory activity: Any out of home activity that is not work or school, including maintenance activities such as shopping as well as discretionary activities such as out-of-home recreation and eating out.
Fully joint tour: A tour in which two or more household members travel together to all out-of-home activity locations and return home together. In other words, no household member is picked-up or dropped-off en route.
Intermediate stop: An out-of-home activity location on the tour other than the anchor location or the primary destination. Intermediate stops are made on the way from the anchor location to the primary destination (outbound) or on the way from the primary destination back to the anchor location (inbound).
Tour mode: The \u201cmain mode\u201d or \u201cpreferred mode\u201d of the tour. This is an abstract concept used categorize the tour with respect to accessibility and constrain the availability of modes for trips on the tour to ensure some consistency of modes used for each trip.
The resident model design is shown below.
The first model in the sequence is disaggregate accessibilities. This is a recent addition to ActivitySim in which the tour destination choice model is run for a prototypical sample population covering key market segments and destination choice logsums from the model are written out for each tour in the population. These destination choice logsums are then merged with the actual synthetic population and used as accessibility variables in downstream models such as auto ownership, coordinated daily activity patterns, and tour frequency. are mandatory location choice; this model is run for all workers and students regardless of whether they attend work or school on the simulated day.
Next a set of long-term and mobility models are run. The first model in the sequence predicts whether an autonomous vehicle is owned by the household. This model conditions the next model, which predicts the number of autos owned. If an autonomous vehicle is owned, multiple cars are less likely. Next, the mandatory (work and school) location choice models are run. The work location choice models includes a model to predict whether the worker has a usual out-of-home work location or exclusively works from home. If the worker chooses to work from home, they will not generate a work tour. An external worker identification model determines whether each worker with an out-of-home workplace location works within the region or external to the region. If they work external to the region, the external station is identified. Any primary destination of any work tours generated by the worker will be the external station chosen by this model. A work location choice model predicts the internal work location of each internal worker, and a school location choice model predicts the school location of each student.
Next, a set of models predicts whether workers and students have subsidized transit fares and if so, the percent of transit fare that is subsidized, and whether each person in the household owns a transit pass. A vehicle type choice model then runs, which predicts the body type, fuel type, and age of each vehicle owned by the household; this model was extended to predict whether each vehicle is autonomous, conditioned by the autonomous vehicle ownership model.
Next, we predict whether each household has access to a vehicle transponder which can be used for managed lane use. We assume that all vehicles built after a certain year (configurable by the user) are equipped with transponders. Next we predict whether each worker has subsidized parking available at work. Finally we predict the telecommute frequency of each worker, which affects downstream models including the daily activity pattern model, the non-mandatory tour frequency model, and stop frequency models.
Next the daily and tour level models are run. The first daily model is the daily activity pattern model is run, which predicts the general activity pattern type for every household member. Then Mandatory tours are generated for workers and students, the tours are scheduled (their location is already predicted by the work/school location choice model), a vehicle availability model is run that predicts which household vehicle would be used for the tour, and the tour mode is chosen. After mandatory tours are generated, a school pickup/dropoff model forms half-tours where children are dropped off and/or picked up at school. The model assigns chaperones to drive or ride with children, groups children together into \u201cbundles\u201d for ride-sharing, and assigns the chaperone task to either a generated work tour or generates a new tour for the purpose of ridesharing. Fully joint tours \u2013 tours where two or more household members travel together for the entire tour - are generated at a household level, their composition is predicted (adults, children or both), the participants are determined, the vehicle availability model is run, and a tour mode is chosen. The primary destination of fully joint tours is predicted, the tours are scheduled, the vehicle availability model is run, and a tour mode is chosen. Next, non-mandatory tours are generated, their primary destination is chosen, they are scheduled, the vehicle availability model is run, and a tour mode is chosen for each. At-work subtours are tours that start and end at the workplace. These are generated, scheduled (with constraints that the start and end times must nest within the start and end time of the parent work tour), a primary destination is selected, the vehicle availability model is run, and a tour mode is chosen.
The major tour modes are shown below:
flowchart TD\n subgraph four\n direction LR\n E[Ride-Hail] --> E1[Taxi]\n E --> E2[Single-pay TNC]\n E --> E3[Shared TNC];\n end\n subgraph three\n direction LR\n D[Transit] --> D1[Walk Access]\n D --> D2[PNR Access]\n D --> D3[KNR Access]\n D --> D4[TNC Access]\n\n D1 --> D11[Local Only]\n D1 --> D12[Premium Only]\n D1 --> D13[Mixed]\n\n D2 --> D21[Local Only]\n D2 --> D22[Premium Only]\n D2 --> D23[Mixed]\n\n D3 --> D31[Local Only]\n D3 --> D32[Premium Only]\n D3 --> D33[Mixed]\n\n D4 --> D41[Local Only]\n D4 --> D42[Premium Only]\n D4 --> D43[Mixed]\n end\n subgraph two\n direction LR\n C[Active] --> C1[Walk]\n C --> C2[Bike]\n C --> C3[E-Scooter]\n C --> C4[E-Bike]\n end\n\n subgraph one\n direction LR\n B[Auto] --> B1[Drive-alone];\n B --> B2[Shared 2];\n B --> B3[Shared 3+];\n end\n A[Tour Mode] --> one;\n A --> two;\n A --> three;\n A --> four;\n\n classDef group1 fill:#f75f5f,stroke:#333,stroke-width:4px,font-size:26px,font-weight:bold;wrap\n classDef group2 fill:#ffd966,stroke:#333,stroke-width:2px,font-size:20px;wrap\n classDef group3 fill:#bdabf0,stroke:#333,stroke-width:2px,font-size:18px;wrap\n\n classDef hiddenTitle color:transparent;\n class one,two,three,four hiddenTitle;\n\n class A group1;\n class B,C,D,E group2;\n class B1,B2,B3,C1,C2,C3,C4,D1,D2,D3,D4,E1,E2,E3,D11,D12,D13,D21,D22,D23,D31,D32,D33,D41,D42,D43 group3;\n
At this point, all tours are generated, scheduled, have a primary destination, and a selected tour mode. The next set of models fills in details about the tours - number of intermediate stops, location of each stop, the departure time of each stop, and the mode of each trip on the tour. Finally, the parking location of each auto trip to the central business district (CBD) is determined. After the model is run, the output files listed above are created. The trip lists are then summarized into origin-destination matrices by time period and vehicle class or transit mode and assigned to the transport network.
The Overnight Visitor Model simulates trips of visitors staying overnight in hotels, motels, short-term vacation rentals, and with friends and family. The trips are modeled as part of tours that begin and end at the place of lodging. However, unlike the resident model, the Overnight Visitor Model does not utilize a 24-hour activity schedule. Therefore it can be thought of as a simpler, tour-based model. Once each tour is generated, it is scheduled independently. The model uses the same time periods and modes as the resident model. The overall design of the model is shown in the figure below.
Tour Enumeration: A list of visitor parties is generated from the input household data and hotel room inventory at the MGRA level. Visitor travel parties by segment (business versus personal) are calculated based on separate rates for hotels and for households. Visitor parties are generated by purpose based on tour rates by segment, then attributed with household income and party size based on input distributions. There are three purposes in the Overnight Visitor model:
Work: Business travel made by business visitors.
Dining: Travel to food establishments for both business and personal visitors.
Recreational: All other non-work non-food related activities.
Tour Level Models
2.1 Tour Scheduling Probabilistic: The tour scheduling model uses a probabilistic draw of the scheduling distribution. This model assigns start and end times to the tour. If there is only one trip per leg on the tour, the trip is assigned the tour start/end time.
2.2 Tour Destination Choice: The destination choice model chooses the MGRA of the primary activity location on the tour.
2.3 Tour Mode Choice: The tour mode choice model determines the primary mode of the tour.
Stop Level Models
3.1 Stop Frequency Choice: The stop frequency model predicts the number of stops by direction based on input distributions that vary by tour purpose.
3.2 Stop Purpose: The stop purpose model chooses the activity purpose of each intermediate stop based on input distributions that vary according to tour purpose.
3.3 Stop Location Choice: The location choice model chooses the MGRA for each intermediate stop on the tour.
Trip Level Models
4.1 Trip Departure Choice: The trip scheduling model assigns depart times for each trip on a tour based on input distributions that vary by direction (inbound versus outbound), stop number, and number of periods remaining on the tour.
4.2 Trip Mode Choice: Each trip is assigned a trip mode, consistent with the modes in the resident model.
4.3 Trip Assignment: Trips are aggregated by time of day, mode occupancy, value-of-time, and origin-destination TAZ and assigned simultaneously with other trips.
The major tour modes are shown below:
flowchart TD\n subgraph four\n direction LR\n E[Ride-Hail] --> E1[Taxi]\n E --> E2[Single-pay TNC];\n end\n subgraph three\n direction LR\n D[Transit] --> D1[Walk Access]\n\n D1 --> D11[Local Only]\n D1 --> D12[Premium Only]\n D1 --> D13[Mixed]\n end\n subgraph two\n direction LR\n C[Active] --> C1[Walk]\n end\n\n subgraph one\n direction LR\n B[Auto] --> B1[Drive-alone];\n B --> B2[Shared 2];\n B --> B3[Shared 3+];\n end\n A[Tour Mode] --> one;\n A --> two;\n A --> three;\n A --> four;\n\n classDef group1 fill:#f75f5f,stroke:#333,stroke-width:4px,font-size:26px,font-weight:bold;wrap\n classDef group2 fill:#ffd966,stroke:#333,stroke-width:2px,font-size:20px;wrap\n classDef group3 fill:#bdabf0,stroke:#333,stroke-width:2px,font-size:18px;wrap\n\n classDef hiddenTitle color:transparent;\n class one,two,three,four hiddenTitle;\n\n class A group1;\n class B,C,D,E group2;\n class B1,B2,B3,C1,C2,C3,C4,D1,D2,D3,D4,E1,E2,E3,D11,D12,D13,D21,D22,D23,D31,D32,D33,D41,D42,D43 group3;\n\n
For more information on the Overnight Visitor Travel Model see technical documentation.
Model outputs are written to the data lake immediately following ABM3 model run completion.
Output CSV files are converted to Parquet format before writing to the data lake.
Each model run is assigned a unique scenario ID.
Data lake files are loaded to Delta tables:
Each output file in the data lake is loaded into its corresponding Delta table. For example, the trips output file is loaded into the trips Delta table, the persons output file is loaded into the persons Delta table, etc.
Delta tables store the results from all model runs, organized by scenario ID.
Delta Tables are processed in Databricks:
Delta tables are read, transformed, and aggregated as needed to support analysis and reporting requirements.
Once transformations are complete, the resulting data is written back to the data lake as new Delta tables or used to update existing tables.
These new Delta tables are also organized by scenario ID, making it easier to manage and query specific versions of processed data.
Delta tables are ingested by Power BI:
Power BI reads the data from the Delta tables.
Power BI report templates with various metrics of interest are automatically refreshed with new model run outputs.
Metrics can easily be compared across different scenario IDs.
"},{"location":"design/supply/highway-skims-assign.html","title":"Highway Skimming and Assignment","text":"
Details of highway skimming and assignment.
"},{"location":"design/supply/network-import-tned.html","title":"Network Import from TNED","text":"
This section describes the procedure by which the ABM3 model system imports (into Emme) network (highway and transit) files along with a general description of the different network files.
The ABM3 model system has been configured to be compatible with SANDAG\u2019s Transportation Network Editing Database (TNED) system, which is utilized to edit, maintain and generate transportation networks. The TNED network files, generated via an ETL (i.e., Extract, Tranform, Load) procedure, serve as inputs to the ABM3 model system\u2019s import network procedure and are produced in text file, shapefile, geodatabase table and geodatabase feature class geodatabase formats. There are, additionally, some non-TNED input network files which are manually maintained.
The following are the required network files used during the Emme import network procedure:
File Source Description EMMEOutputs.gdb/TNED_HwyNet TNED Roadway network links EMMEOutputs.gdb/TNED_HwyNodes TNED Roadway network nodes EMMEOutputs.gdb/TNED_RailNet TNED Rail network links EMMEOutputs.gdb/TNED_RailNodes TNED Rail network nodes EMMEOutputs.gdb/Turns TNED Turn prohibition records special_fares.txt Manually Maintained Special fares in terms of boarding and incremental in-vehicle costs timexfer_{time_of_day}.csv Manually Maintained Timed transfer pairs of lines, by period. Where time_of_day refers to EA, AM, MD, PM, or EV. trrt.csv TNED Attribute data (modes, headways) for the transit lines trlink.csv TNED Sequence of links (routing) for the transit lines trstop.csv TNED Stop data for the transit lines MODE5TOD.csv Manually Maintained Global (per-mode) transit cost and perception attributes vehicle_class_toll_factors.csv Manually Maintained Factors to adjust the toll cost by facility name and class"},{"location":"design/supply/network-import-tned.html#import-network-procedure","title":"Import Network Procedure","text":"
This section describes the main steps carried out during the Emme import network procedure. The entire process is executed by the import_network.py script.
This step creates the different combinations of traffic and transit modes that will get applied to the network links. A mode defines a group of vehicles or users which have access to the same parts of the network. Modes are used in both the traffic and transit assignments to define the available network for each class of demand. Each mode is uniquely identified by a single case-sensitive character. The modes which have access to a given link are listed on that link, and each link must allow at least one mode.
"},{"location":"design/supply/network-import-tned.html#create-roadway-base-network","title":"Create Roadway Base Network","text":"
This step creates the base roadway network by importing it from the EMMEOutputs.gdb/TNED_HwyNet and EMMEOutputs.gdb/TNED_HwyNodes. The nodes and links (referred to as the base network in Emme) for the traffic network are imported from the TNED_HwyNode and TNED_HwyNet geodatabase feature classes. The nodes are created first and the links connect between them. The I-node (from node, field AN) and J-node (to node, field BN) are used to associate the nodes and links and uniquely identify the link in the Emme network. Separate forward (AB) and reverse (BA) links are generated for links that have been coded as two-way.
This step processes the EMMEOutputs.gdb/Turns input network file to generate turn restrictions by to- and from- link ID. If the indicated link IDs do not make a valid turn (links not adjacent) an error is reported.
This step calculates derived traffic attributes. It utilizes the vehicle_class_toll_factors.csv to adjust toll costs by facility name and class.
"},{"location":"design/supply/network-import-tned.html#check-zone-access","title":"Check Zone Access","text":"
This step verifies that every centroid has at least one available access and egress connector.
"},{"location":"design/supply/network-import-tned.html#create-rail-base-network","title":"Create Rail Base Network","text":"
This step creates the base roadway network by importing it from the EMMEOutputs.gdb/TNED_RailNet and EMMEOutputs.gdb/TNED_RailNodes. The nodes and links (referred to as the base network in Emme) for the rail network are imported from the TNED_RailNode and TNED_RailNet geodatabase feature classes. The nodes are created first and the links connect between them. The I-node (from node, field AN) and J-node (to node, field BN) are used to associate the nodes and links and uniquely identify the link in the Emme network. Separate forward (AB) and reverse (BA) links are generated for links that have been coded as two-way.
This step creates the transit lines by importing them from the trrt.csv, trlink.csv and trstop.csv input network files and matched to the transit base network. The mode-level attributes from MODE5TOD.csv, which vary by mode, are copied to transit line attributes and used in transit assignment. It is in this step also where the timexfer_{time_of_day}.csv files are used to explicitly set route-to-route specific transfer transit times.
The transit line and stop / segment attributes (including fares) are imported to Emme attributes. The special_fares.txt lists network-level incremental fares by boarding (line and/or stop) and in-vehicle segment. They specify additive fares based on the network elements encountered on a transit journey and are used to represent the Coaster (or other) zonal fare system.
"},{"location":"design/supply/transit-skims-assign.html","title":"Transit Skimming and Assignment","text":"
The transit assignment uses a headway-based approach, where the average headway between vehicle arrivals for each transit line is known, but not exact schedules. Passengers and vehicles arrive at stops randomly and passengers choose their travel itineraries considering the expected average waiting time.
The Emme Extended transit assignment is based on the concept of optimal strategy but extended to support a number of behavioral variants. The optimal strategy is a set of rules which define sequence(s) of walking links, boarding and alighting stops which produces the minimum expected travel time (generalized cost) to a destination. At each boarding point the strategy may include multiple possible attractive transit lines with different itineraries. A transit strategy will often be a tree of options, not just a single path. A line is considered attractive if it reduces the total expected travel time by its inclusion. The demand is assigned to the attractive lines in proportion to their relative frequencies.
The shortest \u201ctravel time\u201d is a generalized cost formulation, including perception factors (or weights) on the different travel time components, along with fares, and other costs / perception biases such as transfer penalties which vary over the network and transit journey.
The model has four access modes to transit (walk, park-and-ride (PNR), kiss-and-ride (KNR), and Transportation Network Company (TNC)) and three transit sets (local bus only, premium transit only, and local bus and premium transit sets), for 12 total demand classes by 5 TOD. These classes are assigned by slices, one at a time, to produce the total transit passenger flows on the network.
While there are 12 slices of demand, there are only three classes of skims: Local bus only, premium only, and all modes. The access mode does not change the assignment parameters or skims.
"}]}
\ No newline at end of file
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Welcome to the SANDAG Activity-Based Travel Model documentation site!
This website describes the travel demand modeling system developed by the San Diego Association of Governments (SANDAG). SANDAG plans for many complex mobility issues facing the San Diego region, including development of the Regional Plan. Transportation models are complex analysis tools used to provide transportation planners and policymakers with information to help allocate scarce resources fairly and equitably. As we plan for the future, models are used to forecast potential future scenarios of where people will live and how they will travel. They are the principal tool used for alternatives analysis.
The SANDAG transportation model is an activity-based model (ABM). It simulates individual and household transportation decisions that make up their daily travel. This includes all trips people make on a daily basis, such as to work, school, shopping, healthcare, and recreation. An ABM provides a controlled, analytical platform so that different inputs and alternatives can be evaluated to predict whether, when, and how this travel occurs. SANDAG ABM accounts for a variety of different weekday travel markets in the region, including San Diego region resident travel, travel by Mexico residents and other travelers crossing San Diego County\u2019s borders, visitor travel, airport passengers at both the San Diego International Airport and the Cross Border Xpress bridge to the Tijuana International Airport, and commercial travel.
The most recent version of the SANDAG ABM is referred to as \u201cABM3\u201d, and was developed for use in the 2025 Regional Plan. ABM3 is a significant enhancement from ABM2+ which was used for the 2021 Regional Plan. All of the passenger demand models in ABM2+ were converted from CT-RAMP to ActivitySim, including resident travel, cross-border travel, visitor travel, and airport travel. The internal-external travel component is now fully integrated with the resident model. The model was also enhanced to improve the representation of household and person-based mobility, vehicle fleet ownership, transit, shared and private micro-mobility, and micro-transit. Many of the model components were re-estimated using household survey data collected in 2022, and all model components were re-calibrated to base-year 2023 conditions. A new disaggregate commercial vehicle model was developed based upon a 2020 commercial vehicle survey, and implemented in ActivitySim.
This website includes a description of the model system, how to set up and run the models, and a description of model inputs and outputs. Some aspects of the site are a work-in-progress, so we recommend that you check in often, and share your thoughts on ways to improve the site with SANDAG Transportation Modeling staff. Thank you!
"},{"location":"cvm.html","title":"Commercial Vehicle Model","text":""},{"location":"cvm.html#design","title":"Design","text":""},{"location":"cvm.html#inputs","title":"Inputs","text":""},{"location":"cvm.html#outputs","title":"Outputs","text":""},{"location":"faq.html","title":"FAQs","text":""},{"location":"faq.html#how-many-abm-versions-does-sandag-maintain","title":"How many ABM versions does SANDAG maintain?","text":"
There are four released ABM versions - ABM1, ABM2, ABM2+, and ABM3.
"},{"location":"faq.html#who-uses-the-sandag-abm-and-what-for","title":"Who uses the SANDAG ABM and what for?","text":"
The SANDAG ABM is used by SANDAG and many other public and private entities in the San Diego region. These entities include the City of San Diego and other local jurisdictions, Caltrans District 11, San Diego Metropolitan Transit System, North County Transit District, and private developers. Typical ABM applications include analysis for regional planning, air quality conformity, corridor studies, and land use development impact studies.
"},{"location":"faq.html#what-model-components-does-the-sandag-abm-have","title":"What model components does the SANDAG ABM have?","text":"
The SANDAG ABM is a suite of models covering various travel demand markets in the San Diego region. The microsimulation model components include a San Diego resident model, a commercial vehicle model, a Mexican resident crossborder model, a visitor model, a San Diego International Airport ground access model, a Cross-Border Express model serving Tijuana International Airport, and a special event model. The aggregate model components include an external heavy truck model and external trip models.
"},{"location":"faq.html#what-is-the-base-year-of-the-sandag-abm","title":"What is the base year of the SANDAG ABM?","text":"
ABM2 has a base year of 2022. ABM1 base year was 2012 and both ABM2 and ABM2+ have a base year of 2016.
"},{"location":"inputs.html","title":"ABM3 Model Inputs","text":"
The main inputs to ABM3 include the transportation network, land-use data, synthetic population data, parameters files, and model specifications. Outputs include a set of files that describe travel decisions made by all travel markets considered by the model (residents, overnight visitors, airport ground access trips, commercial vehicles and trucks, Mexico residents traveling in San Diego County, and travel made by all other non-residents into and through San Diego County).
There are several file types used for model inputs and outputs. They are denoted by their extension, as listed in the table below.
Extension Format .log, .txt Text files created during a model run containing logging results. .yaml Text files used for setting properties that control ActivitySim or some other process. .csv Comma-separated value files used to store model parameters, input or output data. .omx Open matrix format files used to store input or output trip tables or skims .h5 HDF5 files, used to store pipeline for restarting ActivitySim .shp (along with other files - .cpg, .dbf, .prj, .shx) ArcGIS shapefiles and associated files .html Hypertext markup language files, open in web browser .png Portable network graphics file, open in web browser, Microsoft photos, or third-party graphics editor"},{"location":"inputs.html#model-inputs","title":"Model Inputs","text":"
The table below contains brief descriptions of the input files required to execute the SANDAG ABM3.
File Name Purpose File Type Prepared By Land Use mgra_based_input{SCENARIO_YEAR}.csv Land use forecast of the size and structure of the region\u2019s economy and corresponding demographic forecast CSV Land Use Modelers, Transportation Modelers, and GIS activity_code_indcen_acs.csv PECAS activity code categories mapping to Census industry codes; This is used for military occupation mapping. CSV Land Use Modelers pecas_occ_occsoc_acs.csv PECAS activity code categories mapping to Census industry codes CSV Lande Use Modelers mobilityHubMGRA.csv CSV Transportation Modelers Synthetic Population households.csv Synthetic households CSV Transportation Modelers persons.csv Synthetic persons CSV Transportation Modelers Network: Highway (to be updated with TNED) hwycov.e00 Highway network nodes from GIS ESRI input exchange Transportation Modelers hwycov.e00 Highway network links from GIS ESRI input exchange Transportation Modelers turns.csv Highway network turns file CSV Transportation Modelers LINKTYPETURNS.dbf Highway network link type turns table DBF Transportation Modelers LINKTYPETURNSCST.DBF DBF Transportation Modelers vehicle_class_toll_factors.csv Relative toll values by six vehicle classes by Facility name. Used to identify \u201cfree for HOV\u201d type managed lane facilities. CSV Transportation Modelers off_peak_toll_factors.csv Relative toll values for the three off-peak times-of-day (EA, MD, EV) by Facility name. Multiplied together with the values from vehicle_class_toll_factors.csv to get the final toll. CSV Transportation Modelers vehicle_class_availability.csv The availability / unavailability of six vehicle classes for five times-of-day by facility name. CSV Transportation Modelers Network: Transit (To be updated with TNED) trcov.e00 Transit network arc data from GIS ESRI input exchange Transportation Modelers trcov.e00 Transit network node data from GIS ESRI input exchange Transportation Modelers trlink.csv Transit route with a list of links file CSV Transportation Modelers trrt.csv Transit route attribute file CSV Transportation Modelers trstop.csv Transit stop attribute file TCSV Transportation Modelers mode5tod.csv Transit mode parameters table CSV Transportation Modelers timexfer_XX.csv Transit timed transfers table between COASTER and feeder buses; XX is the TOD (EA, AM, MD, PM, and EV) CSV Transportation Modelers special_fares.txt Fares to coaster Text File Transportation Modelers Network: Active Transportation SANDAG_Bike_Net.dbf Bike network links DBF GIS SANDAG_Bike_Node.dbf Bike network nodes DBF GIS bikeTazLogsum.csv (not saved in inputs, instead, run at the beginning of a model run) Bike TAZ logsum CSV Transportation Modelers bikeMgraLogsum.csv (not saved in inputs, instead, run at the beginning of a model run) Bike MGRA logsum CSV Transportation Modelers walkMgraEquivMinutes.csv (not saved in inputs, instead, run at the beginning of a model run) Walk, in minutes, between MGRAs CSV Visitor Model (Derived from visitor survey) visitor_businessFrequency.csv Visitor model tour frequency distribution for business travelers CSV Transportation Modelers visitor_personalFrequency.csv Visitor model tour frequency distribution for personal travelers CSV Transportation Modelers visitor_partySize.csv Visitor model party size distribution CSV Transportation Modelers visitor_autoAvailable.csv Visitor model auto availability distribution CSV Transportation Modelers visitor_income.csv Visitor model income distribution CSV Transportation Modelers visitor_tourTOD.csv Visitor model tour time-of-day distribution CSV Transportation Modelers visitor_stopFrequency.csv Visitor model stop frequency distribution CSV Transportation Modelers visitor_stopPurpose.csv Visitor model stop purpose distribution CSV Transportation Modelers visitor_outboundStopDuration.csv Visitor model time-of-day offsets for outbound stops CSV Transportation Modelers visitor_inboundStopDuration.csv Visitor model time-of-day offsets for inbound stops CSV Transportation Modelers Airport Model (Derived from airport survey) airport_purpose.csv Airport model tour purpose frequency table CSV Transportation Modelers airport_party.csv Airport model party type frequency table CSV Transportation Modelers airport_nights.csv Airport model trip duration frequency table CSV Transportation Modelers airport_income.csv Airport model trip income distribution table CSV Transportation Modelers airport_departure.csv Airport model time-of-day distribution for departing trips CSV Transportation Modelers airport_arrival.csv Airport model time-of-day distribution for arriving trips CSV Transportation Modelers Cross-Border Model (Derived from cross-border survey) crossBorder_tourPurpose_control.csv CSV crossBorder_tourPurpose_nonSENTRI.csv Cross Border Model tour purpose distribution for Non-SENTRI tours CSV Transportation Modelers crossBorder_tourPurpose_SENTRI.csv Cross Border Model tour purpose distribution for SENTRI tours CSV Transportation Modelers crossBorder_tourEntryAndReturn.csv Cross Border Model tour entry and return time-of-day distribution CSV Transportation Modelers crossBorder_supercolonia.csv Cross Border Model distance from Colonias to border crossing locations CSV Transportation Modelers crossBorder_pointOfEntryWaitTime.csv Cross Border Model wait times at border crossing locations table CSV GIS - Pat L vtsql crossBorder_stopFrequency.csv Cross Border Model stop frequency data CSV Transportation Modelers crossBorder_stopPurpose.csv Cross Border Model stop purpose distribution CSV Transportation Modelers crossBorder_outboundStopDuration.csv Cross Border Model time-of-day offsets for outbound stops CSV Transportation Modelers crossBorder_inboundStopDuration.csv Cross Border Model time-of-day offsets for inbound stops CSV Transportation Modelers External Models (Derived from SCAG survey) externalExternalTripsByYear.csv (raw inputs have these by year) External origin-destination station trip matrix CSV Transportation Modelers externalInternalControlTotalsByYear.csv (raw inputs have these by year) External Internal station control totals read by GISDK CSV Transportation Modelers internalExternal_tourTOD.csv Internal-External Model tour time-of-day frequency distribution CSV Transportation Modelers Commercial Vehicle Model (TO BE UPDATED) tazcentroids_cvm.csv Zone centroid coordinates in state plane feet and albers CSV Transportation Modelers commVehFF.csv Commercial Vehicle Model friction factors CSV Transportation Modelers OE.csv Commercial vehicle model parameters file for off-peak early (OE) period CSV Transportation Modelers AM.csv Commercial vehicle model parameters file for AM period CSV Transportation Modelers MD.csv Commercial vehicle model parameters file for mid-day (MD) period CSV Transportation Modelers PM.csv Commercial vehicle model parameters file for PM period CSV Transportation Modelers OL.csv Commercial vehicle model parameters file for off-peak late (OL) period CSV Transportation Modelers FA.csv Commercial vehicle model parameters file for fleet allocator (FA) industry CSV Transportation Modelers GO.csv Commercial vehicle model parameters file for government/ office (GO) industry CSV Transportation Modelers IN.csv Commercial vehicle model parameters file for industrial (IN) industry CSV Transportation Modelers FA.csv Commercial vehicle model parameters file for fleet allocator (FA) industry CSV Transportation Modelers RE.csv Commercial vehicle model parameters file for retail (RE) industry CSV Transportation Modeler SV.csv Commercial vehicle model parameters file for service (SV) industry CSV Transportation Modelers TH.csv Commercial vehicle model parameters file transport and handling (TH) industry CSV Transportation Modelers WH.csv Commercial vehicle model parameters file wholesale (WH) industry CSV Transportation Modelers Truck Model TruckTripRates.csv Truck model data: Truck trip rates CSV Transportation Modelers regionalEItrips.csv Truck model data: Truck external to internal data CSV Transportation Modelers regionalIEtrips.csv Truck model data: Truck internal to external data CSV Transportation Modelers regionalEEtrips.csv Truck model data: Truck external to external data CSV Transportation Modelers specialGenerators.csv Truck model data: Truck special generator data CSV Transportation Modelers Other parametersByYears.csv Parameters by scenario years. Includes AOC, aiport enplanements, cross-border tours, cross-border sentri share. CSV Transportation Modelers filesByYears.csv File names by scenario years. CSV Transportation Modelers trip_XX.omx Warm start trip table; XX is the TOD (EA, AM, MD, PM, and EV) OMX Transportation Modelers zone.term TAZ terminal times Space Delimited Text File Transportation Modelers
MGRA_BASED_INPUT<<SCENARIO_YEAR>>.CSV
Column name Description mgra MGRANumber taz TAZ Number luz_id pop total population hhp total household population (exclude gq pop) hs housing structures hs_sf single family structures hs_mf multi family structures hs_mh mobile homes hh total number of households hh_sf number of households - single family hh_mf number of households - multi family hh_mh number of mobile homes hhs household size gq_civ GQ civilian gq_mil GQ military i1 Number of households with income less than \\(15,000 (\\)2010) i2 Number of households with income \\(15,000-\\)29,999 ($2010) i3 Number of households with income \\(30,000-\\)44,999 ($2010) i4 Number of households with income \\(45,000-\\)59,999 ($2010) i5 Number of households with income \\(60,000-\\)74,999 ($2010) i6 Number of households with income \\(75,000-\\)99,999 ($2010) i7 Number of households with income \\(100,000-\\)124,999 ($2010) i8 Number of households with income \\(125,000-\\)149,999 ($2010) i9 Number of households with income \\(150,000-\\)199,999 ($2010) i10 Number of households with income \\(200,000 or more (\\)2010) emp_gov Government employment emp_mil military employment emp_ag_min Agriculture and mining employment emp_bus_svcs Professional and Business Services employment emp_fin_res_mgm Financial and resource management employment emp_educ Education services employment emp_hlth Health services employment emp_ret Retail services employment emp_trn_wrh Transportation and Warehousing employment emp_con Construction employment emp_utl Utilities office support employment emp_mnf Manufacturing employment emp_whl Wholesale employment emp_ent Entertainment services employment emp_accm Hotel and accomodation services emp_food Food services employment emp_oth Other employment emp_non_ws_wfh Non-wage and salary work from home employments emp_non_ws_oth Non-wage and salary other employments emp_total Total employment pseudomsa Pseudo MSA - 1: Downtown 2: Central 3: North City 4: South Suburban 5: East Suburban 6: North County West 7: North County East 8: East County zip09 2009 Zip Code enrollgradekto8 Grade School K-8 enrollment enrollgrade9to12 Grade School 9-12 enrollment collegeenroll Major College enrollment othercollegeenroll Other College enrollment hotelroomtotal Total number of hotel rooms parkactive Acres of Active Park openspaceparkpreserve Acres of Open Park or Preserve beachactive Acres of Active Beach district27 milestocoast Distance (miles) to the nearest coast acres Total acres in the mgra (used in CTM) land_acres Acres of land in the mgra (used in CTM) effective_acres Effective acres in the mgra (used in CTM) truckregiontype exp_hourly Expected hourly prking cost exp_daily Expected daily prking cost exp_monthly Expected monthly prking cost parking_type MGRA parking type parking_spaces MGRA estimated parking spaces ech_dist Elementary school district hch_dist High school district remoteAVParking Remote AV parking available at MGRA: 0 = Not available, 1 = Available refueling_stations Number of refueling stations at MGRA MicroAccessTime Micro-mobility access time (mins) microtransit microtransit access time (mins) nev Neighborhood Electric Vehicle access time (mins) totint Total intersections duden Dwelling unit density empden Employment density popden Population density retempden Retail employment density totintbin Total intersection bin empdenbin Employment density bin dudenbin Dwelling unit density bin PopEmpDenPerMi Population and employment density per mile "},{"location":"inputs.html#activity-mapping-to-industry-codes","title":"Activity Mapping to Industry Codes","text":""},{"location":"inputs.html#activity_code_indcen_acscsv","title":"ACTIVITY_CODE_INDCEN_ACS.CSV","text":"Column Name Description indcen Industry code defined in PECAS: They are about 270 industry categories grouped by 6-digit NAICS code (North American Industrial Classification System) activity_code Activity code defined in PECAS. They are about 30 types of activities grouped by the industry categories:1 = Agriculture3 = Construction Non-Building office support (including mining)5 = Utilities office support9 = Manufacturing office support10 = Wholesale and Warehousing11 = Transportation Activity12 = Retail Activity13 = Professional and Business Services14 = Professional and Business Services (Building Maintenance)16 = Private Education Post-Secondary (Post K-12) and Other17 = Health Services18 = Personal Services Office Based19 = Amusement Services20 = Hotels and Motels21 = Restaurants and Bars22 = Personal Services Retail Based23 = Religious Activity24 = Private Households25 = State and Local Government Enterprises Activity27 = Federal Non-Military Activity28 = Federal Military Activity30 = State and Local Government Non-Education Activity office support31 = Public Education"},{"location":"inputs.html#pecas-soc-defined-occupational-codes","title":"PECAS SOC - Defined Occupational Codes","text":""},{"location":"inputs.html#pecas_occ_occsoc_acscsv","title":"PECAS_OCC_OCCSOC_ACS.CSV","text":"Column Name Description occsoc5 Detailed occupation codes defined by the Standard Occupational Classification (SOC) system commodity_id Commodity code defined in PECAS. The detailed SOC occupations are grouped into 6 types of laborers, which are included as part of commodity: 51 = Services Labor 52 = Work at Home Labor 53 = Sales and Office Labor 54 = Natural Resources Construction and Maintenance Labor 55 = Production Transportation and Material Moving Labor 56 = Military Labor"},{"location":"inputs.html#listing-of-external-zones-attributes","title":"Listing of External Zones Attributes","text":""},{"location":"inputs.html#externalzonesxls","title":"EXTERNALZONES.XLS","text":"Column Name Description Internal Cordon LUZ Internal Cordon Land use zone External LUZ External land use zone Cordon Point Cordon Point description Destination Approximation Name of approximate city destination Miles to be Added to Cordon Point Miles to be added to cordon point Travel Time Travel time to external zone Border Delay Border delay time Minutes to be Added to Cordon Point Minutes to be added to cordon point MPH Average miles per hour based on miles and minutes to be added to cordon point"},{"location":"inputs.html#population-synthesizer-household-data","title":"Population Synthesizer Household Data","text":""},{"location":"inputs.html#householdscsv","title":"HOUSEHOLDS.CSV","text":"Column Name Description hhid Unique Household ID household_serial_no Household serial number taz TAZ of household mgra MGRA of household hinccat1 Household income category:1 = <$30k2 = $30-60k3 = $60-100k4 = $100-150k5 = $150k+ hinc Household income num_workers Number of workers in household veh Number of vehicles in household persons Number of persons in household hht Household/family type:0 = Not in universe (vacant or GQ)1 = Family household: married-couple2 = Family household: male householder, no wife present3 = Family household: female householder, no husband present4 = Nonfamily household: male householder, living alone5 = Nonfamily household: male householder, not living alone6 = Nonfamily household: female householder, living alone7 = Nonfamily household: female householder, not living alone bldgsz Building size - Number of Units in Structure & Quality:1 = Mobile home or trailer2 = One-family house detached3 = One-family house attached8 = 20-49 Apartments9 = 50 or more apartments unittype Household unit type:0 = Non-GQ Household1 = GQ Household version Synthetic population run version. Presently set to 0. poverty Poverty indicator utilized for social equity reports. Percentage value where value <= 2 (200% of the Federal Poverty Level) indicates household is classified under poverty."},{"location":"inputs.html#population-synthesizer-person-data","title":"Population Synthesizer Person Data","text":""},{"location":"inputs.html#personscsv","title":"PERSONS.CSV","text":"Column Name Description hhid Household ID perid Person ID Household_serial_no Household serial number pnum Person Number age Age of person sex Gender of person1 = Male2 = Female military Military status of person:0 = N/A Less than 17 Years Old1 = Yes, Now on Active Duty pemploy Employment status of person:1 = Employed Full-Time2 = Employed Part-Time3 = Unemployed or Not in Labor Force4 = Less than 16 Years Old pstudent Student status of person:1 = Pre K-122 = College Undergrad+Grad and Prof. School3 = Not Attending School ptype Person type:1 = Full-time Worker2 = Part-time Worker3 = College Student4 = Non-working Adult5 = Non-working Senior6 = Driving Age Student7 = Non-driving Student8 = Pre-school educ Educational attainment:1 = No schooling completed9 = High school graduate13 = Bachelor\u2019s degree grade School grade of person:0 = N/A (not attending school)2 = K to grade 85 = Grade 9 to grade 126 = College undergraduate occen5 Occupation:0 = Not in universe (Under 16 years or LAST-WRK = 2)1..997 = Legal census occupation code occsoc5 Detailed occupation codes defined by the Bureau of Labor Statistics"},{"location":"inputs.html#highway-network-vehicle-class-toll-factors-file","title":"Highway Network Vehicle Class Toll Factors File","text":""},{"location":"inputs.html#vehicle_class_toll_factorscsv","title":"vehicle_class_toll_factors.csv","text":"
Required file. Used to specify the relative toll values by six vehicle classes by Facility name, scenario year and time of day. Can be used, for example, to identify \u201cfree for HOV\u201d type managed lane facilties. Used by the Import network Modeller tool.
Example:
Facility_name Year Time_of_Day DA_Factor S2_Factor S3_Factor TRK_L_Factor TRK_M_Factor TRK_H_Factor I-15 2016 EA 1.0 0.0 0.0 1.0 1.03 2.33 SR-125 2016 ALL 1.0 1.0 1.0 1.0 1.03 2.33 I-5 2035 ALL 1.0 1.0 0.0 1.0 1.03 2.33
The toll values for each class on each link are calculated by multiplying the input toll value from hwycov.e00 (ITOLLA, ITOLLP, ITOLLO) by this factor, matched by the Facility name (together with the toll factors from off_peak_toll_factors.csv in converting ITOLLO to the off-peak times-of-day).
The network links are matched to a record in this file based on the NM, FXNM or TXNM values (in that order). A simple substring matching is used, so the record with Facility_name \u201cI-15\u201d matches any link with name \u201cI-15 SB\u201d, \u201cI-15 NB\u201d, \u201cI-15/DEL LAGO DAR NB\u201d etc. The records should not be overlapping: if there are two records which match a given link it will be an arbitrary choice as to which one is used.
Note that if a link does not match to a record in this file, the default factors (specified in the table below) will be applied to said link. It is OK if there are records for which there are no link tolls.
Column Name Description Facility_name Name of the facility, used in the substring matching with links by NM, FXNM or TXNM fields Year Scenario year Time_of_Day Time of day period: EA = Early morning (3am - 5:59am) AM = AM peak (6am to 8:59am) MD = Mid-day (9am to 3:29pm) PM = PM peak (3:30pm to 6:59pm) EV = Evening (7pm to 2:59am) ALL = All time of day periods DA_Factor Positive toll factor for Drive Alone (SOV) vehicle classes. The default value is 1.0 S2_Factor Positive toll factor for Shared 2 person (HOV2) vehicle classes. The default value is 1.0 S3_Factor Positive toll factor for Shared 3+ person (HOV3) vehicle classes. The default value is 1.0 TRK_L_Factor Positive toll factor for Light Truck (TRKL) vehicle classes. The default value is 1.0 TRK_M_Factor Positive toll factor for Medium Truck (TRKM) vehicle classes. The default value is 1.03 TRK_H_Factor Positive toll factor for Heavy Truck (TRKH) vehicle classes. The default value is 2.03 "},{"location":"inputs.html#highway-network-off-peak-toll-factors-file","title":"Highway Network Off-Peak Toll Factors File","text":""},{"location":"inputs.html#off_peak_toll_factorscsv","title":"off_peak_toll_factors.csv","text":"
Optional file. Used to specify different tolls in the off-peak time-of-day scenarios based on the single link ITOLLO field, together with the tolls by vehicle class from vehicle_class_toll_factors.csv. Used by the Import network Modeller tool.
See note re: network link matching under vehicle_class_toll_factors.csv. Note that all facilities need not be specified, links not matched will use a factor of 1.0.
Column Name Description Facility_name Name of the facility, used in the substring matching with links by NM, FXNM or TXNM fields OP_EA_FACTOR Positive toll factor for Early AM period tolls OP_MD_FACTOR Positive toll factor for Midday period tolls OP_EV_FACTOR Positive toll factor for Evening period tolls "},{"location":"inputs.html#highway-network-vehicle-class-toll-factors-file_1","title":"Highway Network Vehicle Class Toll Factors File","text":""},{"location":"inputs.html#vehicle_class_availabilitycsv","title":"vehicle_class_availability.csv","text":"
Optional file. Specifies the availability / unavailability of six vehicle classes for five times-of-day by Facility name. This will override any mode / vehicle class availability specified directly on the network (hwycov.e00), via ITRUCK and IHOV fields. Used in the generation of time-of-day Emme scenarios in the Master run Modeller tool.
See note re: network link matching under vehicle_class_toll_factors.csv. Note that all facilities need not be specified, links not matched will use the availability as indicated by the link fields in hwycov.e00.
Column Name Description Facility_name Name of the facility, used in the substring matching with links by NM, FXNM or TXNM fields Vehicle_class Name of the vehicle class, one of DA, S2, S3, TRK_L, TRK_M, or TRK_H EA_Avail For this facility and vehicle class, is available for Early AM period (0 or 1) AM_Avail For this facility and vehicle class, is available for AM Peak period (0 or 1) MD_Avail For this facility and vehicle class, is available for Midday period (0 or 1) PM_Avail For this facility and vehicle class, is available for PM Peak period (0 or 1) EV_Avail For this facility and vehicle class, is available for Evening period (0 or 1)"},{"location":"inputs.html#special_farestxt","title":"special_fares.txt","text":"
"},{"location":"inputs.html#transit-binary-stop-table","title":"Transit Binary Stop Table","text":""},{"location":"inputs.html#trstopcsv","title":"TRSTOP.CSV","text":"Column Name Description Stop_id Unique stop ID Route_id Sequential route number Link_id Link id associated with route Pass_count Number of times the route passes this stop. Most of value is one, some value is 2 Milepost Stop mile post Longitude Stop Longitude Latitude Stop Latitude NearNode Node number that stop is nearest to FareZone Zones defined in Fare System StopName Name of Stop MODE_NAME Line haul mode name: Transfer Center City Walk Walk Access Commuter Rail Light Rail Regional BRT (Yellow) Regional BRT (Red) Limited Express Express Local MODE_ID Mode ID 1 = Transfer 2 = Center City Walk 3 = Walk Access 4 = Commuter Rail 5 = Light Rail 6 = Regional BRT (Yellow) 7 = Regional BRT (Red) 8 = Limited Express 9 = Express 10 = Local PREMODE Premium Transit mode 0 = No 1 = Yes EXPBSMODE Express bus mode 0 = No 1 = Yes LOCMODE Local bus mode 0 = No 1 = Yes OP_TRNTIME Off peak transcad matrix used by mode: *oploctime *oppretime AM_TRNTIME AM peak transcad matrix used by mode: *amloctime *ampretime PM_TRNTIME PM peak transcad matrix used by mode: *pmloctime *pmpretime MODE_ACCES Mode of access (1) MODE_EGRES Mode of egress (1) WT_IVTPK Weight for peak in-vehicle time: 1, 1.5, or 1.8 WT_FWTPK Weight for peak first wait time: 1, 1.5 WT_XWTPK Weight for peak transfer wait time: 1, 3 WT_FAREPK Weight for peak fare: 0.46, 0.60, 0.63, 0.67, 1 WT_IVTOP Weight for off-peak in-vehicle time: 1, 1.5, or 1.6 WT_FWTOP Weight for off-peak first wait time: 1, 1.5 WT_XWTOP Weight for off-peak transfer wait time: 1, 3 WT_FAREOP Weight for off-peak fare: 0.23, 0.51, 0.52, 0.54, 0.58, 1 FARE Transit fare: $0, $1.25, $1.50, $2.50, $3.00, $3.50 DWELLTIME Dwell time: 0, 0.3, 0.5 FARETYPE Fare Type: 1 = Bus 2 = Rail FAREFIELD Fare Field: coaster fare lightrail fare CRMODE Boolean if Commuter rail available LRMODE Boolean if light rail available XFERPENTM Transfer Penalty time: 5 minutes WTXFERTM Transfer Wait time: 1 minute TRNTIME_EA Early AM transit time impedance TRNTIME_AM AM transit time impedance TRNTIME_MD Midday transit time impedance TRNTIME_PM PM transit time impedance TRNTIME_EV Evening transit time impedance"},{"location":"inputs.html#transit-timed-transfers-between-coaster-and-feeder-buses","title":"Transit Timed Transfers Between COASTER and Feeder Buses","text":""},{"location":"inputs.html#timexfer_xxcsv","title":"TIMEXFER_XX.CSV","text":"Column Name Description FROM_LINE From Route Number TO_LINE To Route Number WAIT_TIME Wait time in minutes"},{"location":"inputs.html#transit-stop-table","title":"Transit Stop Table","text":""},{"location":"inputs.html#trstopcsv_1","title":"TRSTOP.CSV","text":"Column Name Description Stop_id Unique stop ID Route_id Sequential route number Link_id Link id associated with route Pass_count Number of times the route passes this stop. Most of value is one, some value is 2 Milepost Stop mile post Longitude Stop Longitude Latitude Stop Latitude NearNode Node number that stop is nearest to FareZone Zones defined in Fare System StopName Name of Stop MODE_NAME Line haul mode name:TransferCenter City WalkWalk AccessCommuter RailLight RailRegional BRT (Yellow)Regional BRT (Red)Limited ExpressExpressLocal MODE_ID Mode ID1 = Transfer2 = Center City Walk3 = Walk Access4 = Commuter Rail5 = Light Rail6 = Regional BRT (Yellow)7 = Regional BRT (Red)8 = Limited Express9 = Express10 = Local PREMODE Premium Transit mode0 = No1 = Yes EXPBSMODE Express bus mode0 = No1 = Yes LOCMODE Local bus mode0 = No1 = Yes OP_TRNTIME Off peak transcad matrix used by mode:oploctimeoppretime AM_TRNTIME AM peak transcad matrix used by mode:amloctimeampretime PM_TRNTIME PM peak transcad matrix used by mode:pmloctimepmpretime MODE_ACCES Mode of access (1) MODE_EGRES Mode of egress (1) WT_IVTPK Weight for peak in-vehicle time: 1, 1.5, or 1.8 WT_FWTPK Weight for peak first wait time: 1, 1.5 WT_XWTPK Weight for peak transfer wait time: 1, 3 WT_FAREPK Weight for peak fare: 0.46, 0.60, 0.63, 0.67, 1 WT_IVTOP Weight for off-peak in-vehicle time: 1, 1.5, or 1.6 WT_FWTOP Weight for off-peak first wait time: 1, 1.5 WT_XWTOP Weight for off-peak transfer wait time: 1, 3 WT_FAREOP Weight for off-peak fare: 0.23, 0.51, 0.52, 0.54, 0.58, 1 FARE Transit fare: $0, $1.25, $1.50, $2.50, $3.00, $3.50 DWELLTIME Dwell time: 0, 0.3, 0.5 FARETYPE Fare Type:1 = Bus2 = Rail FAREFIELD Fare Field:coaster farelightrail fare CRMODE Boolean if Commuter rail available LRMODE Boolean if light rail available XFERPENTM Transfer Penalty time: 5 minutes WTXFERTM Transfer Wait time: 1 minute TRNTIME_EA Early AM transit time impedance TRNTIME_AM AM transit time impedance TRNTIME_MD Midday transit time impedance TRNTIME_PM PM transit time impedance TRNTIME_EV Evening transit time impedance"},{"location":"inputs.html#transit-link-file","title":"Transit Link File","text":""},{"location":"inputs.html#trlinkcsv","title":"TRLINK.CSV","text":"Column Name Description Route_id: Sequential route number Link_id Link id associated with route Direction + or -"},{"location":"inputs.html#bike-network-link-field-list","title":"Bike Network Link Field List","text":""},{"location":"inputs.html#sandag_bike_netdbf","title":"SANDAG_BIKE_NET.DBF","text":"Column Name Description ROADSEGID Road Segment ID RD20FULL Road/Street Name A Foreign key of first node B Foreign key of second node A_LEVEL Level of first node B_LEVEL Level of second node Distance Arc length of link (ft) AB_Gain Cumulative non-negative increase in elevation from A to B nodes (ft) BA_Gain Cumulative non-negative increase in elevation from B to A nodes (ft) ABBikeClas Type of Bike Classification in AB direction where:1 = Multi-Use Path2 = Bike Lane3 = Bike Route BABikeClas Type of Bike Classification in BA direction where:1 = Multi-Use Path2 = Bike Lane3 = Bike Route AB_Lanes Number of Lanes in AB direction BA_Lanes Number of Lanes in BA direction Func_Class Type of Road Functional Class where:1 = Freeway to Freeway Ramp2 = Light (2-lane) Collector Street3 = Rural Collector Road4 = Major Road/4-lane Major Road5 = Rural Light Collector/Local Road6 = Prime Arterial7 = Private Street8 = Recreational Parkway9 = Rural Mountain RoadA = AlleyB = Class I Bicycle PathC = Collector/4-lane Collector StreetD = Two-lane Major StreetE = ExpresswayF = FreewayL = Local Street/Cul-de-sacM = Military Street within BaseP = Paper StreetQ = UndocumentedR = Freeway/Expressway On/Off RampS = Six-lane Major StreetT = TransitwayU = Unpaved RoadW = Pedestrian Way/Bikeway Bike2Sep Separated Bike Lane Flag where:0 = No1 = Yes Bike3Blvd Bike Boulevard Lane Flag where:0 = No1 = Yes SPEED Road Speed A_Elev A Node Elevation B_Elev B Node Elevation ProjectID Project ID in the regional bike network Year Year built/opened to the public Scenicldx Scenic index represents the closeness to the ocean and parks Path Null Shape_Leng length of the link (ft)"},{"location":"inputs.html#bike-network-node-field-list","title":"Bike Network Node Field List","text":""},{"location":"inputs.html#sandag_bike_nodedbf","title":"SANDAG_BIKE_NODE.DBF","text":"Column Name Description NodeLev_ID Node Unique Identifier MGRA MGRA ID for Centroids TAZ TAZ ID for Centroids TAP TAP ID XCOORD X Coordinate of Node in NAD 1983 State Plane California Region VI FIPS: 0406 (ft) YCOORD Y Coordinate of Node in NAD 1983 State Plane California Region VI FIPS: 0406(ft) ZCOORD Elevation (ft) Signal Traffic Signal Presence where: 0 = Absence 1 = Presence"},{"location":"inputs.html#zone-terminal-time","title":"Zone Terminal Time","text":""},{"location":"inputs.html#zoneterm","title":"ZONE.TERM","text":"Column Name Description Zone TAZ number Terminal time Terminal time (3, 4, 5, 7, 10 minutes)"},{"location":"inputs.html#bike-taz-logsum","title":"Bike TAZ Logsum","text":""},{"location":"inputs.html#biketazlogsumcsv","title":"BIKETAZLOGSUM.CSV","text":"Column Name Description i Origin TAZ j Destination TAZ Logsum Logsum - a measure of the closeness of the origin and the destination of the trip time Time (In minutes)"},{"location":"inputs.html#bike-mgra-logsum","title":"Bike MGRA Logsum","text":""},{"location":"inputs.html#bikemgralogsumcsv","title":"BIKEMGRALOGSUM.CSV","text":"Column Name Description i Origin of MGRA j Destination of MGRA Logsum Logsum - a measure of the closeness of the origin and the destination of the trip time Time (in minutes)"},{"location":"inputs.html#walk-mgra-equivalent-minutes","title":"Walk MGRA Equivalent Minutes","text":""},{"location":"inputs.html#walkmgraequivminutescsv","title":"WALKMGRAEQUIVMINUTES.CSV","text":"Column Name Description i Origin (MGRA) j Destination (MGRA) percieved Percieved time to walk actual Actual time to walk (minutes) gain Gain in elevation"},{"location":"inputs.html#airport-trip-purpose-distribution","title":"Airport Trip Purpose Distribution","text":""},{"location":"inputs.html#airport_purposesancsv-and-airport_purposecbxcsv","title":"AIRPORT_PURPOSE.SAN.CSV AND AIRPORT_PURPOSE.CBX.CSV","text":"Column Name Description Purpose Trip Purpose: 0 = Resident Business 1 = Resident Personal 2 = Visitor Business 3 = Visitor Personal 4 = External Percent Distribution of Trips in trip purpose"},{"location":"inputs.html#airport-party-size-by-purpose-distribution","title":"Airport Party Size by Purpose Distribution","text":""},{"location":"inputs.html#airport_partysancsv-and-airport_partycbxcsv","title":"AIRPORT_PARTY.SAN.CSV AND AIRPORT_PARTY.CBX.CSV","text":"Column Name Description Party Party size (0 through 5+) purp0_perc Distribution for Resident Business purpose purp1_perc Distribution for Resident Personal purpose purp2_perc Distribution for Visitor Business purpose purp3_perc Distribution for Visitor Personal purpose purp4_perc Distribution for External purpose"},{"location":"inputs.html#airport-number-of-nights-by-purpose-distribution","title":"Airport Number of Nights by Purpose Distribution","text":""},{"location":"inputs.html#airport_nightssancsv-and-airport_nightscbxcsv","title":"AIRPORT_NIGHTS.SAN.CSV AND AIRPORT_NIGHTS.CBX.CSV","text":"Column Name Description Nights Number of Nights stayed (0 through 14+) purp1_perc Distribution for Resident Personal purpose purp2_perc Distribution for Visitor Business purpose purp3_perc Distribution for Visitor Personal purpose purp4_perc Distribution for External purpose"},{"location":"inputs.html#airport-income-by-purpose-distribution","title":"Airport Income by Purpose Distribution","text":""},{"location":"inputs.html#airport_incomesancsv-and-airport_incomecbxcsv","title":"AIRPORT_INCOME.SAN.CSV AND AIRPORT_INCOME.CBX.CSV","text":"Column Name Description Income group Household income: 0 = Less than $25K 1 = $25K \u2013 $50K 2 = $50K \u2013 $75K 3 = $75K \u2013 $100K 4 = $100K \u2013 $125K 5 = $125K \u2013 $150K 6 = $150K \u2013 $200K 7 = $200K plus purp1_perc Distribution for Resident Personal purpose purp2_perc Distribution for Visitor Business purpose purp3_perc Distribution for Visitor Personal purpose purp4_perc Distribution for External purpose"},{"location":"inputs.html#airport-departure-time-by-purpose-distribution","title":"Airport Departure Time by Purpose Distribution","text":""},{"location":"inputs.html#airport_departuresancsv-and-airport_departurecbxcsv","title":"AIRPORT_DEPARTURE.SAN.CSV and AIRPORT_DEPARTURE.CBX.CSV","text":"Column Name Description Period Departure Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM purp1_perc Distribution for Resident Personal purpose purp2_perc Distribution for Visitor Business purpose purp3_perc Distribution for Visitor Personal purpose purp4_perc Distribution for External purpose"},{"location":"inputs.html#airport-arrival-time-by-purpose-distribution","title":"Airport Arrival Time by Purpose Distribution","text":""},{"location":"inputs.html#airport_arrivalsancsv-and-airport_arrivalcbxcsv","title":"AIRPORT_ARRIVAL.SAN.CSV and AIRPORT_ARRIVAL.CBX.CSV","text":"Column Name Description Period Arrival Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM purp1_perc Distribution for Resident Personal purpose purp2_perc Distribution for Visitor Business purpose purp3_perc Distribution for Visitor Personal purpose purp4_perc Distribution for External purpose"},{"location":"inputs.html#cross-border-model-tour-entry-and-return-distribution","title":"Cross Border Model Tour Entry and Return Distribution","text":""},{"location":"inputs.html#crossborder_tourentryandreturncsv","title":"CROSSBORDER_TOURENTRYANDRETURN.CSV","text":"Column Name Description Purpose Tour Purpose: 0 = Work 1 = School 2 = Cargo 3 = Shop 4 = Visit 5 = Other EntryPeriod Entry Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM Return Period Return Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM Percent Distribution of tours in entry and return period time slots"},{"location":"inputs.html#cross-border-model-supercolonia","title":"Cross Border Model Supercolonia","text":""},{"location":"inputs.html#crossborder_supercoloniacsv","title":"CROSSBORDER_SUPERCOLONIA.CSV","text":"Column Name Description Supercolonia_ID Super colonia ID Population Population of the super colonia Distance_poe0 Distance from colonia to point of entry 0 (San Ysidro) Distance_poe1 Distance from colonia to point of entry 1 (Otay Mesa) Distance_poe2 Distance from colonia to point of entry 2 (Tecate)"},{"location":"inputs.html#cross-border-model-point-of-entry-wait-time","title":"Cross Border Model Point of Entry Wait Time","text":""},{"location":"inputs.html#crossborder_pointofentrywaittimecsv","title":"CROSSBORDER_POINTOFENTRYWAITTIME.CSV","text":"Column Name Description poe Point of Entry number: 0 = San Ysidro 1 = Otay Mesa 2 = Tecate 3 = Otay Mesa East 4 = Jacumba StartHour Start Hour (1 through 12) EndHour End Hour (1 through 12) StartPeriod Start Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM EndPeriod End Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM StandardWait Standard wait time SENTRIWait SENTRI users wait time PedestrianWait Pedestrian wait time"},{"location":"inputs.html#cross-border-model-stop-frequency","title":"Cross Border Model Stop Frequency","text":""},{"location":"inputs.html#crossborder_stopfrequencycsv","title":"CROSSBORDER_STOPFREQUENCY.CSV","text":"Column Name Description Purpose Tour Purpose: 0 = Work 1 = School 2 = Cargo 3 = Shop 4 = Visit 5 = Other DurationLo Lower bound of tour duration (0, 4, or 8) DurationHi Upper bound of tour duration (4, 8, or 24) Outbound Number of stops on the outbound (0, 1, 2, 3+) Inbound Number of stops on the inbound (0, 1, 2, 3+) Percent Distribution of tours by purpose, duration, number of outbound/inbound stops"},{"location":"inputs.html#cross-border-model-stop-purpose-distribution","title":"Cross Border Model Stop Purpose Distribution","text":""},{"location":"inputs.html#crossborder_stoppurposecsv","title":"CROSSBORDER_STOPPURPOSE.CSV","text":"Column Name Description TourPurp Tour Purpose: 0 = Work 1 = School 2 = Cargo 3 = Shop 4 = Visit 5 = Other Inbound Boolean for whether stop is inbound (0=No, 1=Yes) StopNum Stop number on tour (1, 2, or 3) Multiple Boolean for whether there are multiple stops on tour (0=No, 1=Yes) StopPurp0 Distribution of Work stops StopPurp1 Distribution of School stops StopPurp2 Distribution of Cargo stops StopPurp3 Distribution of Shopping stops StopPurp4 Distribution of Visiting stops StopPurp5 Distribution of Other stops"},{"location":"inputs.html#cross-border-model-outbound-stop-duration-distribution","title":"Cross Border Model Outbound Stop Duration Distribution","text":""},{"location":"inputs.html#crossborder_outboundstopdurationcsv","title":"CROSSBORDER_OUTBOUNDSTOPDURATION.CSV","text":"Column Name Description RemainingLow Lower bound of remaining half hour periods after last scheduled trip: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM RemainingHigh Upper bound of remaining half hour periods after last scheduled trip: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM Stop Stop number on tour (1, 2, or 3) 0 Probability that stop departure is in same period as last outbound trip 1 Probability that stop departure is in last outbound trip period + 1 2 Probability that stop departure is in last outbound trip period + 2 3 Probability that stop departure is in last outbound trip period + 3 4 Probability that stop departure is in last outbound trip period + 4 5 Probability that stop departure is in last outbound trip period + 5 6 Probability that stop departure is in last outbound trip period + 6 7 Probability that stop departure is in last outbound trip period + 7 8 Probability that stop departure is in last outbound trip period + 8 9 Probability that stop departure is in last outbound trip period + 9 10 Probability that stop departure is in last outbound trip period + 10 11 Probability that stop departure is in last outbound trip period + 11"},{"location":"inputs.html#cross-border-model-inbound-stop-duration-distribution","title":"Cross Border Model Inbound Stop Duration Distribution","text":""},{"location":"inputs.html#crossborder_inboundstopdurationcsv","title":"CROSSBORDER_INBOUNDSTOPDURATION.CSV","text":"Column Name Description RemainingLow Lower bound of remaining half hour periods after last scheduled trip: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM RemainingHigh Upper bound of remaining half hour periods after last scheduled trip: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM Stop Stop number on tour (1, 2, or 3) 0 Probability that stop departure period is same as tour arrival period -1 Probability that stop departure period is tour arrival period - 1 -2 Probability that stop departure period is tour arrival period \u2013 2 -3 Probability that stop departure period is tour arrival period \u2013 3 -4 Probability that stop departure period is tour arrival period \u2013 4 -5 Probability that stop departure period is tour arrival period \u2013 5 -6 Probability that stop departure period is tour arrival period \u2013 6 -7 Probability that stop departure period is tour arrival period - 7"},{"location":"inputs.html#externalexternaltripsbyyearcsv","title":"EXTERNALEXTERNALTRIPSByYEAR.CSV","text":"Column Name Description originTAZ External origin TAZ destinationTAZ External destination TAZ Trips Number of trips between external TAZs"},{"location":"inputs.html#external-internal-control-totals","title":"External Internal Control Totals","text":""},{"location":"inputs.html#externalinternalcontroltotalsbyyearcsv","title":"EXTERNALINTERNALCONTROLTOTALSByYEAR.CSV","text":"Column Name Description Taz External TAZ station Work Number of work vehicle trips Nonwork Number of non-work vehicle trips"},{"location":"inputs.html#internal-external-tours-time-of-day-distribution","title":"Internal External Tours Time of Day Distribution","text":""},{"location":"inputs.html#internalexternal_tourtodcsv","title":"INTERNALEXTERNAL_TOURTOD.CSV","text":"Column Name Description Purpose Tour Purpose: 0 = All Purposes EntryPeriod Entry Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM ReturnPeriod Return Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM Percent Distribution of tours by entry and return periods"},{"location":"inputs.html#parameters-by-scenario-years","title":"Parameters by Scenario Years","text":""},{"location":"inputs.html#parametersbyyearscsv","title":"PARAMETERSBYYEARS.CSV","text":"Column Name Description year Scenario build year aoc.fuel Auto operating fuel cost aoc.maintenance Auto operating maitenance cost airport.SAN.enplanements San Diego International Airport enplanements airport.SAN.connecting San Diego International Airport connecting passengers airport.SAN.airportMgra MGRA San Diego International Airport is located in airport.CBX.enplanements Cross Border Express Terminal (Tijuana International Airport) enplanements airport.CBX.connecting Cross Border Express Terminal (Tijuana International Airport) connecting passengers airport.CBX.airportMgra MGRA Cross Border Express Terminal is located in crossBorder.tours Number of cross border tours crossBorders.sentriShare Share of cross border tours that are SENTRI taxi.baseFare Initial taxi fare taxi.costPerMile Taxi cost per mile taxi.cosPerMinute Taxi cost per minute TNC.single.baseFare Initial TNC fare for single ride TNC.single.costPerMile TNC cost per mile for single ride TNC.single.costPerMinute TNC cost per minute for single ride TNC.single.costMinimum TNC minimum cost for single ride TNC.shared.baseFare Initial TNC fare for shared ride TNC.shared.costPerMile TNC cost per mile for shared ride TNC.shared.costPerMinute TNC cost per minute for shared ride TNC.shared.costMinimum TNC minimum cost for shared ride Mobility.AV.RemoteParkingCostPerHour Remote parking cost per hour for autonomous vehicles active.micromobility.variableCost Variable cost for micromobility active.micromobility.fixedCost Fixed cost for micromobility active.microtransit.fixedCost Fixed cost for microtransit Mobility.AV.Share The share of vehicles assumed to be autonomous vehicles in the vehicle fleet smartSignal.factor.LC smartSignal.factor.MA smartSignal.factor.PA atdm.factor"},{"location":"inputs.html#files-by-scenario-years","title":"Files by Scenario Years","text":""},{"location":"inputs.html#filesbyyearscsv","title":"FILESBYYEARS.CSV","text":"Column Name Description year Scenario build year crossborder.dc.soa.alts.file Crossborder model destination choice alternatives file crossBorder.dc.uec.file Crossborder model destination choice UEC file uwsl.dc.uec.file Tour destination choice UEC file nmdc.uec.file Non-mandatory tour destination choice UEC file crossBorder.tour.mc.uec.file Crossborder model tour mode choice UEC file visualizer.reference.path Path to reference scenario for SANDAG ABM visualizer"},{"location":"inputs.html#mgras-at-mobility-hubs","title":"MGRAs at Mobility Hubs","text":""},{"location":"inputs.html#mobilityhubmgracsv","title":"MOBILITYHUBMGRA.CSV","text":"Column Name Decription MGRA MGRA ID MoHubName Mobility Hub name MoHubType Mobility Hub type: Suburban Coastal Gateway Major Employment Center Urban
Model outputs are stored in the .\\outputs directory. The contents of the directory are listed in the table below.
"},{"location":"outputs.html#output-directory-output","title":"Output Directory (.\\output)","text":"Directory\\File Name Description airport.CBX (directory) Outputs for Cross-Border Express Airport Ground Access Model airport.SAN (directory) Outputs for San Diego International Airport Ground Access Model assignment (directory) Assignment outputs crossborder (directory) Crossborder Travel Model outputs cvm (directory) Commercial Vehicle Model outputs parking (directory) Parking model outputs resident (directory) Resident model outputs skims (directory) Skim outputs visitor (directory) Visitor Model outputs bikeMgraLogsum.csv Bike logsum file for close-together MGRAs bikeTazLogsum.csv Bike logsum file for TAZs datalake_metadata.yaml Metadata file for datalake reporting system derivedBikeEdges.csv Derived bike network edge file derivedBikeNodes.csv Derived bike network node file derivedBikeTraversals.csv Derived bike network traversals file microMgraEquivMinutes.csv Equivalent minutes for using micromobility between close-together MGRAs (not used) runtime_summary.csv Summary of model runtime temp_tazdata_cvm.csv TAZ data for commercial vehicle model transponderModelAccessibilities.csv Transponder model accessibilities (not used) trip_(period).omx Trips for each time period, for assignment walkMgraEquivMinutes.csv Equivalent minutes for walking between close-together MGRAs"},{"location":"outputs.html#skims-skims","title":"Skims (.\\skims)","text":"
This directory contains auto, transit, and non-motorized level-of-service matrices, also known as skims. Each file is a collection of origin destination tables of times and costs, at the TAZ level.
File Description dest_pmsa.omx A matrix containing pseudo - metropolitan statistical area code for each destination TAZ dest_poi.omx A matrix containing point of interest code for each destination TAZ (currently zeros) dest_poi.omx.csv A csv file containing point of interest code for each destination TAZ (currently zeros) impm(truck type)(toll type)_(period)_(matrixtype).txt Truck impedance matrix for truck type (ld = Light duty, lhd = light heavy duty, mhd = medium heavy duty, hhd = heavy heavy duty), toll type (n = non-toll, t = toll) and matrixtype (DU = utility, dist = distance, time = time) maz_maz_bike.csv Bike logsums between close together MGRAs maz_maz_walk.csv Walk times between close together MGRAs maz_stop_walk.csv Walk times between MGRAs and transit stops taz_pmsa_xwalk.csv Crosswalk file between pseudo-metropolitan statistical areas and TAZs traffic_skims_(period).omx Auto skims by period (EA, AM, MD, PM, EV) transit_skims_(period).omx Transit skims by period (EA, AM, MD, PM, EV) "},{"location":"outputs.html#auto-skims-by-period","title":"Auto skims by period","text":"
TRAFFIC_SKIMS_<time period>.OMX
Table Name Description <time_period>_<traffic_class><_vot>_TIME Travel time for evaluation of volume-delay functions <time_period>_<traffic_class><_vot>_DIST Travel distance <time_period>_<traffic_class><_vot>_REL <time_period>_<traffic_class><_vot>_TOLLCOST Total toll cost (only for TOLL traffic classes) <time_period>_<traffic_class><_vot>_TOLLDIST Total distance on toll facilities (only for TOLL traffic classes) <time_period>_<traffic_class><_vot>_MLCOST Total cost for managed lane facilities (only for TOLL traffic classes) <time_period>_<traffic_class><_vot>_HOVDIST Distance on HOV facilities (only for HOV traffic classes) *traffic class = SOV_TR, SOV_NT, HOV2, HOV3, TRK vot = L (low), M (medium), and H (high) "},{"location":"outputs.html#transit-skims-by-period","title":"Transit skims by period","text":"
TRANSIT_SKIMS_<time_period>.OMX
Table Name Description <time_period>_<transit_class>_GENCOST Total generalized cost which includes perception factors from assignment <time_period>_<transit_class>_FIRSTWAIT actual wait time at initial boarding point <time_period>_<transit_class>_XFERWAIT actual wait time at all transfer boarding points <time_period>_<transit_class>_TOTALWAIT total actual wait time <time_period>_<transit_class>_FARE fare paid <time_period>_<transit_class>_XFERS number of transfers <time_period>_<transit_class>_ACCWALK access actual walk time prior to initial boarding <time_period>_<transit_class>_EGRWALK egress actual walk time after final alighting <time_period>_<transit_class>_TOTALWALK total actual walk time <time_period>_<transit_class>_TOTALIVTT Total actual in-vehicle travel time <time_period>_<transit_class>_DWELLTIME Total dwell time at stops <time_period>_<transit_class>_BUSIVTT actual in-vehicle travel time on local bus mode <time_period>_<transit_class>_LRTIVTT actual in-vehicle travel time on LRT mode <time_period>_<transit_class> _CMRIVTT actual in-vehicle travel time on commuter rail mode <time_period>_<transit_class> _EXPIVTT actual in-vehicle travel time on express bus mode <time_period>_<transit_class>_LTDEXPIVTT actual in-vehicle travel time on premium bus mode <time_period>_<transit_class>_BRTIVTT actual in-vehicle travel time on BRT mode *time period = EA, AM, MD, PM, EV transit_class = LOC, PRM, MIX"},{"location":"outputs.html#activitysim-log-files","title":"ActivitySim log files","text":"
ActivitySim writes out various log files when it runs; these have standard names for each model component. Therefore we list them separately, but copies of these files may be in each model\u2019s output directory depending upon the settings used to run ActivitySim for that model component.
File Description activitysim.log ActivitySim log file for model breadcrumbs.yaml Breadcrumbs provides a record of steps that have been run for use when resuming a model run final_checkpoints.csv ActivitySim checkpoint file final_pipeline.h5 ActivitySim pipeline file mem.csv ActivitySim memory use log file mem_mp_households.csv Memory logs for ActivitySim model steps running with the same num_processes (all except accessibility, initialize, and summarize) mem_mp_initialize.csv Memory logs for ActivitySim model step initialize mem_mp_summarize.csv Memory logs for ActivitySim model step summarize mp_households_(processnumber)-activitysim.log ActivitySim log file for processnumber. This logfile is created if model is run in multiprocess mode mp_households_(processnumber)-mem.csv Memory log file for processnumber mp_households_apportion-activitysim.log ActivitySim log file for apportioning data between multiple processes mp_households_coalesce-activitysim.log ActivitySIm logfile for coalesing output from multiple processes into one mp_initialize-activitysim.log ActivitySim log file for the initialization steps mp_initialize-mem.csv Memory logs for ActivitySim model step summarize (similar to mp_initialize-mem.csv) mp_setup_skims-activitysim.log ActivitySim logfile for reading in skims mp_summarize-activitysim.log ActivitySim log file for summarizing model output (omx and csv trip table) mp_summarize-mem.csv Memory logs for ActivitySim model step summarize (similar to mem_mp_initialize.csv) mp_tasks_log.txt Log files of multiprocessed steps omnibus_mem.csv Memory log file of all model steps (similar to mem.csv) run_list.txt List of models that have been run timing_log.csv Model run time by steps"},{"location":"outputs.html#airport-model-outputs-airportcbx-airportsan","title":"Airport model outputs (.\\airport.CBX, .\\airport.SAN)","text":"
There are two subdirectories containing outputs for each of the two airport models. airport.CBX contains output for the Cross-Border Express model, and airport.SAN contains output for the San Diego International Airport model. Each directory has identical files so we provide one generic output table below.
Filename Description final_(airport)accessibility.csv Accessibility file for airport (cbx, san) (not used, created by default) [final_(airport)households.csv](#### Airport Model household file (final_(airport)households.csv)) Household file for airport (cbx, san) final_(airport)land_use.csv Land-use file for airport (cbx, san) [final_(airport)persons.csv](#### Airport Model person file (final_(airport)persons.csv)) Persons file for airport (cbx, san) [final_(airport)tours.csv](#### Airport Model tour file (final_(airport)tours.csv)) Tour file for airport (cbx, san) [final_(airport)trips.csv](#### Airport Model trip file (final_(airport)trips.csv)) Trip file for airport (cbx, san) model_metadata.yaml Datalake metadata file autoairporttrips.(airport)_(period).omx Auto trip table for airport (CBX, SAN) by period (EA, AM, MD, PM, EV) tranairporttrips.(airport)_(period).omx Transit trip table for airport (CBX, SAN) by period (EA, AM, MD, PM, EV) nmotairporttrips.(airport)_(period).omx Non-motorized trip table for airport (CBX, SAN) by period (EA, AM, MD, PM, EV)"},{"location":"outputs.html#airport-model-household-file-final_airporthouseholdscsv","title":"Airport Model household file (final_(airport)households.csv)","text":"Field Description home_zone_id Airport MGRA sample_rate Sample rate household_id Household ID poverty Poverty indicator utilized for social equity reports. Percentage value where value <= 2 (200% of the Federal Poverty Level) indicates household is classified under poverty."},{"location":"outputs.html#airport-model-person-file-final_airportpersonscsv","title":"Airport Model person file (final_(airport)persons.csv)","text":"Field Description household_id Household ID person_id Person ID"},{"location":"outputs.html#airport-model-tour-file-final_airporttourscsv","title":"Airport Model tour file (final_(airport)tours.csv)","text":"Field Description tour_id Tour ID purpose_id ID for tour type:
1 = resident business
2 = resident personal
3= visitor business
4 = visitor personal
5 = external party_size Number of persons in airport travel party nights Number of nights away income Income group 0-7, -99 if employee direction Direction of trip. String. outbound: airport to non-airport, inbound: non-airport to airport household_id Household ID person_id Person ID tour_category Tour category. String \"non_mandatory\" tour_type Type of tour. String. \"Emp\": Employee, \"ext\": External, \"res_busn\": Resident business where n is the ID for the income bracket (1<25K, 2: between 25K & 50K, 3: between 50K & 75K, 4: between 75K & 100K, 5: between 100K & 125K, 6: between 125K & 150K, 7: between 150K & 200K, 8: 200k+
, \"res_pern\": Resident personal where n is the ID for the income bracket as defined above, \"vis_bus\": Visitor business, \"vis_per\": Visitor personal origin Origin MGRA destination Destination MGRA number_of_participants Same as party_size outbound TRUE if outbound, else FALSE start Half-hour time period of departure from tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. end Half-hour time period of arrival back at tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. duration Duration of the tour in number of half-hour periods, including all activity episodes and travel destination_logsum Logsum from destination choice model stop_frequency out_0in, 0out_in primary_purpose \"busn\", \"emp\", \"extn\", \"pern\""},{"location":"outputs.html#airport-model-trip-file-final_airporttripscsv","title":"Airport Model trip file (final_(airport)trips.csv)","text":"Field Description trip_id Trip ID person_id Person ID household_id Household ID primary_purpose Primary purpose of trip: \"busn\": Business, where n is..., \"emp\": Employee, \"extn\": External, where n is..., \"pern\": Personal, where n is... trip_num 1 outbound TRUE if outbound, else FALSE trip_count 1 destination Destination MGRA origin Origin MGRA tour_id Tour ID depart Departure time period (1\u202648) trip_mode Trip mode (see trip mode table) mode_choice_logsum Mode choice logsum for trip vot Value of time in dollars per hour ($2023) arrival_mode Arrival mode from airport trip mode choice model cost_parking Cost of parking ($2023) cost_fare_drive Ridehail/Taxi fare on a trip distance_walk Distance walked on a trip (including access/egress for transit modes) time_mm Micromobility time distance_mm Micromobility distance cost_fare_mm Micromobility cost ($2023) distance_bike Bike distance time_wait_drive Ridehail/Taxi wait times for a trip trip_period A string indicating the skim period for the trip (\u201cEA\u201d,\u201dAM\u201d,\u201dMD\u201d,\u201dPM\u2019,\u201dEV\u201d) party Party size tour_participants Number of joint tour participants if joint tour, else 1 distance_total Trip distance add_driver TRUE if trip requires a driver based on airport mode (for example, TNC, or pickup), else FALSE weight_trip 1 weight_person_trip weight_trip * tour_participants cost_operating_drive Auto operating cost ($2023) inbound TRUE if trip is from (origin) airport to (destination) non-airport zone, else FALSE time_drive Auto time distance_drive Auto distance cost_toll_drive Auto toll cost ($2023) time_transit_in_vehicle Transit in-vehicle time time_rapid_transit_in_vehicle Rapid transit in-vehicle time time_express_bus_transit_in_vehicle Express bus in-vehicle time time_local_bus_transit_in_vehicle Local bus in-vehicle time time_light_rail_transit_in_vehicle Light rail transit in-vehicle time time_commuter_rail_transit_in_vehicle Commuter rail in-vehicle time time_transit_initial_wait Transit initial-wait time cost_fare_transit Transit fare ($2023) transfers_transit Number of transfers time_bike Bike time time_walk Walk mode time cost_total Sum of all costs a trip might incur (auto operating, toll, transit fare) time_total Total travel time (including iIVT and access/egress and wait times for all modes) value_of_time_category_id Value of time bin. 1: Low, 2: Medium, 3: High sample_rate Sample rate otaz Origin TAZ dtaz Destination TAZ"},{"location":"outputs.html#arrival-mode-table-for-airport-models","title":"Arrival Mode Table for Airport Models","text":"Field Description Curb_LOCn Pickup/Dropoff curbside (n=1,5, with 1 = terminal, and 2,5 = other locations) TAXI_LOCn Taxi to airport (n =1,2 with 1= terminal mgra and 2=other) RIDEHAIL_LOCn Ridehail to airport (n =1,2 with 1= terminal mgra and 2=other) PARK_LOCn Parking lot (n=1,5, with 1 = terminal mgra and 2,5= other locations) PARK_ESCORT Parking escort SHUTTLEVAN Shuttle Vehicle RENTAL Rental car HOTEL_COURTESY Hotel transportation WALK Walk WALK_LOC, WALK_PRM, WALK_MIX Walk transit modes KNR_LOC, KNR_PRM, KNR_MIX KNR transit modes TNC_LOC, TNC_PRM, TNC_MIX TNC transit modes"},{"location":"outputs.html#assignment-model-trip-tables-assignment","title":"Assignment model trip tables (.\\assignment)","text":"
This directory contains trip tables from auto and transit assignments.
"},{"location":"outputs.html#demand-matrices","title":"Demand Matrices","text":"File Description autoairportTrips.(airport)_(period_(vot).omx Auto trip table for airport (CBX, SAN) by period (EA, AM, MD, PM, EV) and value of time (low, medium, high) autocrossborderTrips_(period)_(vot).omx Auto trip table for cross border model by period (EA, AM, MD, PM, EV) and value of time (low, medium, high) autoTrips_(period)_(vot).omx Auto trip table for resident model by period (EA, AM, MD, PM, EV) and value of time (low, medium, high) autovisitorTrips_(period)_(vot).omx Auto trip table for visitor model by period (EA, AM, MD, PM, EV) and value of time (low, medium, high) emptyAVTrips.omx Empty private autonomous vehicle trips householdAVTrips.csv All private autonomous vehicle trips TNCTrips.csv All TNC trips TNCVehicleTrips_(period).omx TNC vehicle trip table by period (EA, AM, MD, PM, EV) TranairportTrips.(airport)_(period).omx Transit trip tables for airport (CBX, SAN) by period (EA, AM, MD, PM, EV) TrancrossborderTrips_(period).omx Transit trip tables for cross-border model by period (EA, AM, MD, PM, EV) TranTrips_(period).omx Transit trip tables for resident model by period (EA, AM, MD, PM, EV) TranvisitorTrips_(period).omx Transit trip tables for visitor model by period (EA, AM, MD, PM, EV) TripMatrices.csv Disaggregate commercial vehicle trips"},{"location":"outputs.html#airport-model-auto-demand-matrices","title":"Airport Model auto demand matrices","text":"Table Name Description SR2_<> Shared Ride 2 for <> SR3_<> Shared Ride 3 for <> SOV_<> Drive Alone for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#airport-model-transit-demand-matrices","title":"Airport Model transit demand matrices","text":"Table Name Description WLK_SET_set1_<> PNR_SET_set1_<> KNR_SET_set1_<> TNC_SET_set1_<> WLK_SET_set2_<> PNR_SET_set2_<> KNR_SET_set2_<> TNC_SET_set2_<> WLK_SET_set3_<> PNR_SET_set3_<> KNR_SET_set3_<> TNC_SET_set3_<> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#airport-model-non-motorized-demand-matrices","title":"Airport Model non-motorized demand matrices","text":"Table Name Description WALK_<> Walk for <> BIKE_<> Bike for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#crossborder-model-auto-demand-matrices","title":"Crossborder Model auto demand matrices","text":"Table Name Description SR2_<> Shared Ride 2 for <> SR3_<> Shared Ride 3 for <> SOV_<> Drive Alone for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#crossborder-model-transit-demand-matrices","title":"Crossborder Model transit demand matrices","text":"Table Name Description WLK_SET_set1_<> PNR_SET_set1_<> KNR_SET_set1_<> TNC_SET_set1_<> WLK_SET_set2_<> PNR_SET_set2_<> KNR_SET_set2_<> TNC_SET_set2_<> WLK_SET_set3_<> PNR_SET_set3_<> KNR_SET_set3_<> TNC_SET_set3_<> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#crossborder-model-non-motorized-demand-matrices","title":"Crossborder Model non-motorized demand matrices","text":"Table Name Description WALK_<> Walk for <> BIKE_<> Bike for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#visitor-model-auto-demand-matrices","title":"Visitor Model auto demand matrices","text":"Table Name Description SR2_<> Shared Ride 2 for <> SR3_<> Shared Ride 3 for <> SOV_<> Drive Alone for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#visitor-model-transit-demand-matrices","title":"Visitor Model transit demand matrices","text":"Table Name Description WLK_SET_set1_<> PNR_SET_set1_<> KNR_SET_set1_<> TNC_SET_set1_<> WLK_SET_set2_<> PNR_SET_set2_<> KNR_SET_set2_<> TNC_SET_set2_<> WLK_SET_set3_<> PNR_SET_set3_<> KNR_SET_set3_<> TNC_SET_set3_<> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#visitor-model-non-motorized-demand-matrices","title":"Visitor Model non-motorized demand matrices","text":"Table Name Description WALK_<> Walk for <> BIKE_<> Bike for <> where TIME PERIOD = EA, AM, MD, PM, EV"},{"location":"outputs.html#tnc-vehicle-trip-demand-table","title":"TNC Vehicle trip demand table","text":"Column Name Description trip_ID Trip ID vehicle_ID Vehicle ID originTaz Origin TAZ destinationTaz Destination TAZ originMgra Origin MGRA destinationMgra Destination MGRA totalPassengers Number of passengers in the vehicle startPeriod Trip starting period endPeriod Trip ending period pickupIdsAtOrigin Trip id of the pick-up at origin. CR-RAMP: \u2003\u2003Individual trips: \u2003\u2003\"I_\" + personId + \"_\" + purpAbb + \"_\" + tourid + \"_\" + inbound + \"_\" + stopid \u2003\u2003\u2003where purpAbb is the first 3 letters of the tour_purp field \u2003\u2003Joint trips: \u2003\u2003\"J_\" + hhid + \"_\" + purpAbb + \"_\" + tourid + \"_\" + inbound + \"_\" + stopid + \u201d_\u201d + i \u2003\u2003\u2003where i is a number ranging from 1 to the total number of participants. Visitor trips: \u2003\u2003partySize == 1: \"V_\" + tourid + \"_\" + stopid \u2003\u2003partySize > 1: \"V_\" + tourid + \"_\" + stopid + \u201d_\u201d + i \u2003\u2003\u2003where i is a number ranging from 1 to the total number of participants. Cross-border trips: \u2003\u2003partySize == 1: \"M_\" + tourid + \"_\" + stopid \u2003\u2003partySize > 1: \"M_\" + tourid + \"_\" + stopid + \u201d_\u201d + i \u2003\u2003\u2003where i is a number ranging from 1 to the total number of participants. CBX airport trips: \u2003\u2003partySize == 1: \"CBX_\" + tourid + \"_\" + stopid \u2003\u2003partySize>1: \"CBX_\" + tourid + \"_\" + stopid + \u201d_\u201d + i \u2003\u2003\u2003where i is a number ranging from 1 to the total number of participants. SAN airport trips: \u2003\u2003partySize == 1: \"SAN_\" + tourid + \"_\" + stopid \u2003\u2003partySize > 1: \"SAN_\" + tourid + \"_\" + stopid + \u201d_\u201d + i \u2003\u2003\u2003where i is a number ranging from 1 to the total number of participants. Internal-External trips: \u2003\u2003\"IE_\" + tourid + \"_\" + inbound dropoffIdsAtOrigin Trip id of the drop-off at origin. See pickupIdsAtOrigin for trip id of the trip. pickupIdsAtDestination Trip id of the pick-up at destination. See pickupIdsAtOrigin for trip id of the trip. dropoffIdsAtDestination Trip id of the drop-off at destination. See pickupIdsAtOrigin for trip id of the trip. originPurpose Trip origin purpose destinationPurpose Trip destination purpose"},{"location":"outputs.html#household-autonomous-vehicle-trip-data","title":"Household autonomous vehicle trip data","text":"Column Name Description hh_id Household id veh_id Vehicle id vehicleTrip_id Vehicle trip id orig_mgra Trip origin MGRA dest_gra Trip destination MGRA period Period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM occupants Number of occupants in the vehicle originIsHome Is origin home 0 = No 1 = Yes destinationIsHome Is destination home 0 = No 1 = Yes originIsRemoteParking Is origin remote parking 0 = No 1 = Yes destinationIsRemoteParking Is destination remote parking 0 = No 1 = Yes parkingChoiceAtDestination Parking choice at destination: 0 = Not constrained to remote parking 1 = Park at destination 2 = Remote parking 3 = Park at home person_id Person id person_num Person number tour_id Tour id stop_id Stop id inbound Is trip inbound 1 = Yes 0 = No tour_purpose Tour purpose: Discretionary Eating Out Escort Home Maintenance School Shop University Visiting Work Work-Based orig_purpose Origin trip purpose: Discretionary Eating Out Escort Home Maintenance School Shop University Visiting Work Work-Based Work related dest_purpose Destination trip purpose: Discretionary Eating Out Escort Home Maintenance School Shop University Visiting Work Work-Based Work related trip_orig_mgra Trip origin MGRA trip_dest_mgra Trip destination MGRA stop_period Stop period: 1 = Before 5:00AM 2 = 5:00AM-5:30AM 3 through 39 is every half hour time slots 40 = After 12:00AM periodsUntilNextTrip trip_mode Trip mode: 0 = Empty vehicle trip 1 = Drive Alone 2 = Shared Ride 2 3 = Shared Ride 3"},{"location":"outputs.html#tnc-vehicle-trip-matrix","title":"TNC vehicle trip matrix","text":"Table Name Description TNC_<>_0 TNC trips for <> with 0 passenger TNC_<>_1 TNC trips for <> with 1 passenger TNC_<>_2 TNC trips for <> with 2 passengers TNC_<>_3 TNC trips for <> with 3 or more passengers"},{"location":"outputs.html#empty-autonomous-vehicle-trips-data","title":"Empty Autonomous vehicle trips data","text":"Table Name Description EmptyAV_EA Empty AV trips for EA period EmptyAV_AM Empty AV trips for AM period EmptyAV_MD Empty AV trips for MD period EmptyAV_PM Empty AV trips for PM period EmptyAV_EV Empty AV trips for EV period"},{"location":"outputs.html#crossborder-model-outputs-crossborder","title":"Crossborder model outputs (.\\crossborder)","text":"
This directory contains outputs from the Crossborder model, which represents all travel made by Mexico residents in San Diego County.
File Description final_accessibility.csv Accessibility file for Crossborder Model (not used, created by default) [final_households.csv](#### Crossborder Model household file (final_households.csv)) Household file for Crossborder Model final_land_use.csv Land-use file for Crossborder Model [final_persons.csv](#### Crossborder Model person file (final_persons.csv)) Persons file for Crossborder Model [final_tours.csv](#### Crossborder Model tour file (final_tours.csv)) Tour file for Crossborder Model [final_trips.csv](#### Crossborder Model trip file (final_trips.csv)) Tour file for Crossborder Model model_metadata.yaml Model run meta data for use in Datalake storage and reporting nmCrossborderTrips_AM.omx Non-motorized trip table for Crossborder Model by period (EA, AM, MD, PM, EV) autoCrossborderTrips_AM.omx Auto trip table for Crossborder Model by period (EA, AM, MD, PM, EV) tranCrossborderTrips_AM.omx Transit trip table for Crossborder Model by period (EA, AM, MD, PM, EV) othrCrossborderTrips_AM.omx Other trip table for Crossborder Model by period (EA, AM, MD, PM, EV)"},{"location":"outputs.html#crossborder-model-household-file-final_householdscsv","title":"Crossborder Model household file (final_households.csv)","text":"Field Description sample_rate Sample Rate num_persons Number of persons in travel party origin Origin MGRA (Border crossing station) home_zone_id Home MGRA (Border crossing station) household_id Household ID poverty Poverty indicator utilized for social equity reports. Percentage value where value <= 2 (200% of the Federal Poverty Level) indicates household is classified under poverty."},{"location":"outputs.html#crossborder-model-person-file-final_personscsv","title":"Crossborder Model person file (final_persons.csv)","text":"Field Description household_id Household ID work_time_factor Travel time sensitivity factor for work tours non_work_time_factor Travel time sensitivity factor for non-work tours (Sampled in person preprocessor) origin Origin MGRA (Border crossing station) home_zone_id Home MGRA (Border crossing station) person_id Person ID"},{"location":"outputs.html#crossborder-model-tour-file-final_tourscsv","title":"Crossborder Model tour file (final_tours.csv)","text":"Field Description tour_id Tour ID pass_type Type of border crossing pass. String. \"no_pass\": Does not own a pass, \"sentri\": SENTRI pass, or \"ready\": READY pass tour_type Tour purpose. String. \"other\", \"school\", \"shop\", \"visit\", or \"work\" purpose_id Tour purpose ID. 0: work, 1: school, 2: shop, 3: visit, 4: other tour_category Tour category. String. Mandatory: Work or school, Non-Mandatory: Shop, visit, other number_of_participants Number of participants in tour household_id Household ID person_id Person ID start Half-hour time period of departure from tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. end Half-hour time period of arrival back at tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. duration Duration of the tour in number of half-hour periods, including all activity episodes and travel origin Tour origin (Border Crossing) MGRA destination Tour primary destination MGRA tour_od_logsum Tour origin-crossing-destination logsum poe_id Number of border crossing station tour_mode Tour mode mode_choice_logsum Tour mode choice logsum stop_frequency Number of stops on tour by direction. String. xout_yin where x is number of stops in the outbound direction and y is the number of stops in the inbound direction primary_purpose will drop"},{"location":"outputs.html#crossborder-model-trip-file-final_tripscsv","title":"Crossborder Model trip file (final_trips.csv)","text":"Field Description trip_id Trip ID person_id Person ID household_id Household ID primary_purpose Purpose at primary destination. String. \"other\", \"school\", \"shop\", \"visit\", or \"work\" trip_num Sequential number of trip on half-tour from 1 to 4 outbound TRUE if outbound, else FALSE trip_count number of trips per tour. Will drop destination Destination MGRA origin Origin MGRA tour_id Tour ID purpose Purpose at trip destination. String. \"other\", \"school\", \"shop\", \"visit\", or \"work\" depart Departure time period (1\u202648) trip_mode Trip mode (see trip mode table) trip_mode_choice_logsum Mode choice logsum for trip parking_cost Parking costs at trip origin and destination, calculated as one-half of the costs at each end, with subsidies considered. tnc_single_wait_time Wait time for single pay TNC tnc_shared_wait_time Wait time for shared\\pooled TNC taxi_wait_time Wait time for taxi cost_parking Cost of parking ($2023) cost_fare_drive Taxi/TNC fare (including Taxi/TNC cost of transit access/egress) ($2023) distance_walk Distance walked in miles (including access/egress walk distances of a transit mode) time_mm Micromobility time distance_mm Micromobility distance cost_fare_mm Micromobility cost ($2023) distance_bike Bike distance time_wait_drive Wait times for Taxi/TNC/NEV modes trip_period A string indicating the skim period for the trip (\u201cEA\u201d,\u201dAM\u201d,\u201dMD\u201d,\u201dPM\u2019,\u201dEV\u201d) tour_participants Number of joint tour participants if joint tour, else 1 distance_total Total distance traveled on a trip cost_operating_drive Auto operating cost ($2023) weight_trip Trip weight defined as the ratio of number of particpants on a trip to the assumed occupancy rate of a mode (SHARED2,3) weight_person_trip Person trip weight defined as the ratio of the number of participants on a trip to sample rate of the model run inbound TRUE if trip is in outbound direction, else FALSE time_drive Auto time distance_drive Auto distance cost_toll_drive Auto toll cost ($2023) time_transit_in_vehicle Transit in-vehicle time time_rapid_transit_in_vehicle Rapid transit in-vehicle time time_express_bus_transit_in_vehicle Express bus in-vehicle time time_local_bus_transit_in_vehicle Local bus in-vehicle time time_light_rail_transit_in_vehicle Light rail transit in-vehicle time time_commuter_rail_transit_in_vehicle Commuter rail in-vehicle time time_transit_initial_wait Transit initial-wait time cost_fare_transit Transit fare ($2023) transfers_transit Number of transfers time_bike Bike time time_walk Walk mode time cost_total Sum of all costs a trip might incur (auto operating, toll, transit fare) time_total Total travel time (including iIVT and access/egress and wait times for all modes) value_of_time_category_id Value of time bin. 1: Low, 2: Medium, 3: High origin_micro_prm_dist Distance from trip origin MGRA to closest premium transit stop by microtransit dest_micro_prm_dist Distance from trip destination MGRA to closest premium transit stop by microtransit microtransit_orig Distance from trip origin MGRA to closest local transit stop by microtransit microtransit_dest Distance from trip destination MGRA to closest local transit stop by microtransit microtransit_available TRUE if microtransit is available for trip, else FALSE nev_orig True if Neighborhood Electric Vehicle is available at origin nev_dest True if Neighborhood Electric Vehicle is available at destination nev_available TRUE if Neighborhood Electric Vehicle is available, else FALSE microtransit_access_available_out TRUE if microtransit is available from the origin, else FALSE nev_access_available_out TRUE if neighborhood electric vehicle is available from the origin, else FALSE microtransit_egress_available_out Availability of microtransit egress in the outbound direction nev_egress_available_out Availability of NEV egress in the outbound direction microtransit_access_available_in Availability of microtransit access in the inbound direction nev_access_available_in Availability of NEV egress in the inbound direction microtransit_egress_available_in Availability of microtransit egress in the inbound direction nev_egress_available_in Availability of microtransit egress in the outbound direction sample_rate Sample rate otaz Origin TAZ dtaz Destination TAZ"},{"location":"outputs.html#crossborder-model-tour-mode-definitions","title":"Crossborder Model Tour Mode Definitions","text":"Field Description DRIVEALONE Drive alone SHARED2 Shared ride with 2 participants SHARED3 Shared ride with 3+ participants WALK Walk"},{"location":"outputs.html#commercial-vehicle-model-cvm","title":"Commercial Vehicle Model (.\\cvm)","text":"
This directory contains intermediate files and final expected parking costs calculated from input parking supply data and walk distances between MGRAs.
File Description aggregated_street_data.csv Street length and intersections aggregated to MGRA level, used to estimate free on-street parking spaces cache (directory) Directory containing intermediate calculations for expected parking costs distances.csv MGRA-MGRA distances used for expected parking cost calculations districts.csv Calculated parking districts at MGRA level used for expected parking cost calculations final_parking_data.csv Expected hourly, daily, and monthly parking costs, total spaces, and parking district at the MGRA level for use in travel models plots Directory containing plots of the parking model results shapefiles Directory containing shapefiles for parking model calculations"},{"location":"outputs.html#resident-model-outputs-resident","title":"Resident model outputs (.\\resident)","text":"
This directory contains San Diego resident travel model outputs.
File Description cdap_joint_spec_(persons).csv Model specification file for coordinated daily activity pattern model joint tour alternative for (persons)-way interaction terms cdap_spec_(persons).csv Model specification file for coordinated daily activity pattern model for (persons)-way interaction terms. data_dict.csv Data dictionary for resident model, csv format data_dict.txt Data dictionary for resident model, text format final_accessibility.csv Resident model aggregate accessibility file final_disaggregate_accessibility.csv Resident model disaggregate accessibility file at MGRA level [final_households.csv](#### Resident Model household file (final_households.csv)) Resident model household file [final_joint_tour_participants.csv](#### Resident Model joint tour participants file (final_joint_tour_participants.csv)) Resident model joint tour participants file final_land_use.csv Resident model land-use file [final_persons.csv](#### Resident Model vehicle file (final_vehicles.csv)) Resident model persons file final_proto_disaggregate_accessibility.csv Resident model disaggregate accessibility file at person level [final_tours.csv](#### Resident Model tour file (final_tours.csv)) Resident model tour file [final_trips.csv](#### Resident Model trips file (final_trips.csv)) Resident model trip file [final_vehicles.csv](#### Resident Model vehicle table (final_vehicles.csv)) Resident model vehicle file log (directory) Directory for resident model logging output model_metadata.yaml Resident model Datalake metadata file autoTrips_[tod]_[vot].omx Residential Auto Trip Matrix for 5 time periods (tod = EA, AM, MD, PM, EV) and three value of time bins (vot = low, med, high) tranTrips_[tod].omx Residential Transit Trip Matrix for 5 time periods (tod = EA, AM, MD, PM, EV) nmotTrips_[tod].omx Residential Non-motorized Trip Matrix for 5 time periods (tod = EA, AM, MD, PM, EV) skim_usage.txt Skim usage file trace (directory) Directory for resident model trace output"},{"location":"outputs.html#resident-model-household-file-final_householdscsv","title":"Resident Model household file (final_households.csv)","text":"Field Description home_zone_id Household MGRA - same as mgra income Household income in dollars ($2023) hhsize Number of persons in household HHT Household dwelling unit type. 0: N/A (GQ/vacant), 1: Married couple household, 2: Other family household: Male householder no spouse present, 3: Other family household: Female householder no spouse present, 4: Nonfamily household: Male householder living alone, 5: Nonfamily household: Male householder: Not living alone, 6: Nonfamily household: Female householder: Living alone, 7: Nonfamily household: Female householder: Not living alone auto_ownership (Model output) Auto ownership num_workers Number of workers in household building_category Units in structure. 0: N/A (GQ), 1: Mobile home or trailer, 2: One-family house detached, 3: One-family house attached, 4: 2 Apartments, 5: 3-4 Apartments, 6: 5-9 Apartments, 7: 10-19 Apartments, 8: 20-49 Apartments, 9: 50 or more apartments, 10: Boat, RV, van, etc. unittype Household unit type. 0: Non-GQ Household, 1: GQ Household (used in Visualizer) sample_rate Sample rate for household income_in_thousands Household income in thousands of dollars ($2023) income_segment Household income segment (1-4) num_non_workers Number of non-workers in household num_drivers Number of persons age 16+ num_adults Number of persons age 18+ ebike_owner TRUE if household owns an e-bike, else FALSE (output from e-bike owership simulation) av_ownership TRUE if household owns an autonomous vehicle, else FALSE (output from AV Ownership Model) workplace_location_accessibility Work location choice logsum (output from Disaggregate Accessibility Model) shopping_accessibility Shopping primary destination choice logsum (output from Disaggregate Accessibility Model) othdiscr_accessibility Other Discretionary primary destination choice logsum (output from Disaggregate Accessibility Model) numAVowned Number of autonomous vehicles owned by household (output from Vehicle Type Choice Model) transponder_ownership TRUE if household owns a transponder, else FALSE (output from Transponder Ownership Model) has_joint_tour 1 if household has at least one fully joint tour, else false (output from Coordinated Daily Activity Pattern Model) num_under16_not_at_school Number of persons age less than 16 who do not attend school (output from Coordinated Daily Activity Pattern Model) num_travel_active Number of persons in household who have an active (type M or N) travel pattern (output from Coordinated Daily Activity Pattern Model) num_travel_active_adults Number of adults in household who have an active (type M or N) travel pattern (output from Coordinated Daily Activity Pattern Model) num_travel_active_preschoolers Number of preschool children in household who have an active (type M or N) travel pattern (output from Coordinated Daily Activity Pattern Model) num_travel_active_children Number of children in household who have an active (type M or N) travel pattern (output from Coordinated Daily Activity Pattern Model) num_travel_active_non_preschoolers Number of non-preschoolers household in who have an active (type M or N) travel pattern (output from Coordinated Daily Activity Pattern Model) participates_in_jtf_model TRUE if household has a joint tour frequency model, else FALSE (output from Coordinated Daily Activity Pattern Model) school_escorting_outbound Alternative number for school escort model in the outbound direction (initial output from School Escort Model) school_escorting_inbound Alternative number for school escort model in the inbound direction (output from School Escort Model) school_escorting_outbound_cond Alternative number for school escort model in the outbound direction (final output from School Escort Model) auPkRetail Auto peak access to retail employment from household TAZ (aggregate accessibility output) auPkTotal Auto peak access to total employment from household TAZ (aggregate accessibility output) auOpRetail Auto offpeak access to retail employment from household TAZ (aggregate accessibility output) auOpTotal Auto offpeak access to total employment from household TAZ (aggregate accessibility output) trPkRetail Transit peak access to retail employment from household TAZ (aggregate accessibility output) trPkTotal Transit peak access to total employment from household TAZ (aggregate accessibility output) trPkHH Transit peak access to total employment from household (aggregate accessibility output) trOpRetail Transit offpeak access to retail employment from household TAZ (aggregate accessibility output) trOpTotal Transit offpeak access to total employment from household TAZ (aggregate accessibility output) nmRetail Walk access to retail employment from household TAZ (aggregate accessibility output) nmTotal Walk access to total employment from household TAZ (aggregate accessibility output) microtransit Microtransit access time in household MGRA nev Neighborhood electric vehicle access time in household MGRA mgra Household MGRA - same as home_zone_id TAZ Household TAZ micro_dist_local_bus Distance to closest local bus stop from household MGRA by microtransit, if available. 999999 if not available. micro_dist_premium_transit Distance to closest premium transit stop from household MGRA by microtransit, if available. 999999 if not available. joint_tour_frequency_composition Joint tour frequency and composition model choice (output from Joint Tour Frequency\\Composition Model) num_hh_joint_tours Number of fully joint tours at the household level (0, 1 or 2) (output from Coordinated Daily Activity Pattern Model and Joint Tour Frequency\\Composition Models) household_id Household ID poverty Poverty indicator utilized for social equity reports. Percentage value where value <= 2 (200% of the Federal Poverty Level) indicates household is classified under poverty.
Resident Model person file (final_persons.csv)
Field Description household_id Household ID age Person age in years PNUM Person number in household (1\u2026n where n is number of persons in household) sex 1: Male, 2: Female pemploy Employment status of person. 1: Employed Full-Time, 2: Employed Part-Time, 3: Unemployed or Not in Labor Force, 4: Less than 16 Years Old pstudent Student status of person. 1: Pre K-12, 2: College Undergrad+Grad and Prof. School, 3: Not Attending School ptype Person type 1: Full-time worker 2: Part-time worker 3: College\\University Student 4: Non-Working Adult 5: Retired 6: Driving-age student 7: Non-driving age student 8: Pre-school\\Age <=5 educ Educational attainment. 1: No schooling completed, 9: High school graduate, 13: Bacehlor's degree soc2 Two-digit Standard Occupational Classification (SOC) codes (https://www.bls.gov/oes/current/oes_stru.htm) is_student Person is a K12 or college student school_segment School location choice model's segment a student belongs to (preschool, grade school, high school, university) is_worker Person is a full-time or part-time worker is_internal_worker TRUE if worker works internal to region, else FALSE (output from Internal-External Worker Identification Model) is_external_worker TRUE if worker works external to region, else FALSE (output from Internal-External Worker Identification Model) home_zone_id Household MGRA time_factor_work Travel time sensitivity factor for work tours time_factor_nonwork Travel time sensitivity factor for non-work tours (Sampled in person preprocessor) naics_code Two-digit NAICS code (https://www.census.gov/naics/) occupation Occupation String work_from_home TRUE if worker and works from home, else FALSE (output from Work From Home Model) is_out_of_home_worker TRUE if worker has a usual out of home work location, else FALSE (output from Work From Home Model) external_workplace_zone_id MGRA number of external workplace if external worker, else -1 (output from External Workplace Location Choice Model) external_workplace_location_logsum Location choice logsum for external workplace location choice model (output from External Workplace Location Choice Model) external_workplace_modechoice_logsum Mode choice logsum for mode choice from external workplace location choice model (output from External Workplace Location Choice Model) school_zone_id MGRA number of school location, else -9 (output from School Location Choice Model) school_location_logsum Location choice logsum for school location choice model, else -9 (output from School Location Choice Model) school_modechoice_logsum Mode choice logsum for mode choice from school location choice model, else -9 (output from School Location Choice Model) distance_to_school Distance to school if student, else -9 (output from School Location Choice Model) roundtrip_auto_time_to_school Round trip offpeak auto time to school, else -9 (output from School Location Choice Model) workplace_zone_id MGRA number of internal work location, else -9 (output from Internal Work Location Choice Model) workplace_location_logsum Location choice logsum for work location choice model, else -9 (output from Internal Work Location Choice Model) workplace_modechoice_logsum Mode choice logsum for mode choice from work location choice model, else -9 (output from Internal Work Location Choice Model) distance_to_work Distance to work if internal worker with work location, else -9 (output from Internal Work Location Choice Model) work_zone_area_type Area type of work zone for worker if internal worker with work location, else -9 (output from Internal Work Location Choice Model) auto_time_home_to_work Peak auto time from home to work if internal worker with work location, else -9 (output from Internal Work Location Choice Model) roundtrip_auto_time_to_work Round trip auto travel time to and from work work_auto_savings Travel time savings as a result of using auto vs. walk-transit mode exp_daily_work Expected daily cost of parking at work if internal worker with work location, else -9 (output from Internal Work Location Choice Model) non_toll_time_work Time from home to work for path without I-15, if worker with internal workplace, else -9 toll_time_work Time from home to work for path with I-15, if worker with internal workplace, else -9 toll_dist_work Travel distance for work using a tolled route toll_cost_work Toll cost for going to work toll_travel_time_savings_work Work travel time savings for using tolled vs. non-tolled routes transit_pass_subsidy 1 if person has subsidized transit from their employer or school, else 0 (Output from Transit Subsidy Model) transit_pass_ownership 1 if person owns a transit pass, else 0 (Output from Transit Pass Ownership Model) free_parking_at_work TRUE if person has free parking at work, else FALSE (Output from Free Parking Model) telecommute_frequency Telecommute frequency if worker who does not work from hom, else null (Output from Telecommute Frequency Model) String \"No_Telecommute\", \"1_day_week\", \"2_3_days_week\", \"4_days_week\" cdap_activity Coordinated daily activity pattern type (Output from Coordinated Daily Activity Pattern Model) String \"M\": Mandatory pattern, \"N\": Non-mandatory pattern, \"H\": Home or out of region pattern travel_active TRUE if activity pattern is \"M\" or \"N\", else FALSE (Output from Coordinated Daily Activity Pattern Model) num_joint_tours Total number of fully joint tours (Output from Fully Joint Tour Participation Model) non_mandatory_tour_frequency Non-Mandatory Tour Frequency Model Choice (Output from Non-Mandatory Tour Frequency Chopice Model) num_non_mand Total number of non-mandatory tours (Output from School Escort Model, Non-Mandatory Tour Frequency Model, and At-Work Subtour Model) num_escort_tours Total number of escorting tours (Output from School Escort Model and Non-Mandatory Tour Frequency Model) num_eatout_tours Total number of eating out tours (Output from Non-Mandatory Tour Frequency Model) num_shop_tours Total number of shopping tours (Output from Non-Mandatory Tour Frequency Model) num_maint_tours Total number of other maintenance tours (Output from Non-Mandatory Tour Frequency Model) num_discr_tours Total number of discretionary tours (Output from Non-Mandatory Tour Frequency Model) num_social_tours Total number of social\\visiting tours (Output from Non-Mandatory Tour Frequency Model) num_add_shop_maint_tours Total number of additional shopping and maintenance tours (Output from Non-Mandatory Tour Frequency Extension Model) num_add_soc_discr_tours Total number of additional social\\visiting and other discretionary tours (Output from Non-Mandatory Tour Frequency Extension Model) person_id Person ID miltary 1 if serves in the military, else 0 grade School grade of person: 0 = N/A (not attending school), 2 = K to grade 8, 5 = Grade 9 to grade 12, 6 = College undergraduate weeks Weeks worked during past 12 months 0: N/A (less than 16 years old/did not work during the past 12 .months) 1: 50 to 52 weeks worked during past 12 months 2: 48 to 49 weeks worked during past 12 months 3: 40 to 47 weeks worked during past 12 months 4: 27 to 39 weeks worked during past 12 month 5: 14 to 26 weeks worked during past 12 months 6: 13 weeks or less worked during past 12 months hours Usual hours worked per week past 12 months
0: .N/A (less than 16 years old/did not work during the past .12 months), 1..98 .1 to 98 usual hours, 99 .99 or more usual hours race Recoded detailed race code 1: .White alone, 2: Black or African American alone, 3: American Indian alone, 4: Alaska Native alone, 5: American Indian and Alaska Native tribes specified; or .American Indian or Alaska Native, not specified and no other races, 6: Asian alone, 7: Native Hawaiian and Other Pacific Islander alone, 8: Some Other Race alone, 9: Two or More Races hispanic Hispanic flag: 1: Non-Hispanic, 2: Hispanic
Resident Model vehicle file (final_vehicles.csv)
Field Description vehicle_id Vehicle ID household_id Household ID vehicle_num Vehicle number in household from 1\u2026n where n is total vehicles owned by household vehicle_type String bodytype_age_fueltype auto_operating_cost Auto operating cost for vehicle ($2023 cents/mile) Range Range if electric vehicle, else 0 MPG Miles per gallen for vehicle vehicle_year Year of vehicle vehicle_category String, Body type (Car, Motorcycle, Pickup, SUV, Van. Autonomous vehicles have _AV extension on body type) num_occupants Number of occupants in the vehicle fuel_type String. BEV: Battery electric vehicle, Diesel, Gas, Hybrid: Gas\\Electric non plug-in vehicle, PEV: Plug-in hybrid electric vehicle"},{"location":"outputs.html#resident-model-joint-tour-participants-file-final_joint_tour_participantscsv","title":"Resident Model joint tour participants file (final_joint_tour_participants.csv)","text":"Field Description participant_id Participant ID tour_id Tour ID household_id Household ID person_id Person ID participant_num Sequent number of participant 1\u2026n where n is total number of participants in joint tour"},{"location":"outputs.html#resident-model-tour-file-final_tourscsv","title":"Resident Model tour file (final_tours.csv)","text":"Field Description tour_id Tour ID person_id Person ID tour_type Purpose string of the primary activity on the tour: For home-based tours, the purposes are: \u201cwork\u201d, \u201cschool\u201d, \u201cescort\u201d, \u201cshopping\u201d, \u201cothmaint\u201d, \u201ceatout\u201d, \u201csocial\u201d, and \u201cothdiscr\u201d. For work-based subtours, the purposes are \u201cbusiness\u201d, \u201ceat\u201d, and \u201cmaint\u201d. tour_type_count The total number of tours within the tour_type tour_type_num The sequential number of the tour within the tour_category. In other words if a person has 3 tours; 1 work tour and 2 non-mandatory tours, the tour_type_num would be 1 for the work tour, 1 for the first non-mandatory tour and 2 for the second non-mandatory tour. tour_num Sequential tour ID number for a person tour_count Total number of tours per person tour_category The category string of the primary activity on the tour. \u201cmandatory\u201d, \u201cjoint\u201d, \u201cnon_mandatory\u201d, \u201catwork\u201d number_of_participants Number of participants on the tour for fully joint tours, else 1 destination MGRA number of primary destination origin MGRA number of tour origin household_id Household ID start Half-hour time period of departure from tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. end Half-hour time period of arrival back at tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. duration Duration of the tour in number of half-hour periods, including all activity episodes and travel school_esc_outbound For school tours where the child is being escorted according to the school pickup/dropoff model, this string field indicates the type of escorting in the outbound direction: \u201cpure_escort\u201d or \u201crideshare\u201d school_esc_inbound For school tours where the child is being escorted according to the school pickup/dropoff model, this string field indicates the type of escorting in the inbound direction: \u201cpure_escort\u201d or \u201crideshare\u201d num_escortees Number of children being escorted on this tour (max of outbound and inbound direction) tdd Tour departure and duration. Index of the tour departure and durarion alterntive configs composition Composition of tour if joint \u201cadults\u201d, \u201cchildren\u201d is_external_tour TRUE if primary destination activity is external to region, else FALSE is_internal_tour Whether tour is internal destination_logsum Logsum from tour destination choice model vehicle_occup_1 Tour vehicle with occupancy of 1 vehicle_occup_2 Tour vehicle with occupancy of 2 vehicle_occup_3_5 Tour vehicle with occupancy of 3+ tour_mode Tour mode string mode_choice_logsum Logsum from tour mode choice model selected_vehicle Selected vehicle from vehicle type choice model; a string field consisting of [Body type][age][fuel type] and an optional extension \u201c_AV\u201d if the vehicle is an autonomous vehicle atwork_subtour_frequency At-work subtour frequency choice model result; a string field with the following values: \u201cno_subtours\u201d, \u201cbusiness1\u201d, \u201cbusiness2\u201d, \u201ceat\u201d, \u201ceat_business\u201d, \u201cmaint\u201d, or blank for non-work tours. parent_tour_id Parent tour ID if this is a work-based subtour, else 0 stop_frequency Stop frequency choice model result; a string value of the form [0\u2026n]out_[0\u2026n]in where the first number is the number of outbound stops and the second number is the number of inbound stops primary_purpose Recoding of tour_type where all atwork subtours are identified as \u201catwork\u201d regardless of destination purpose"},{"location":"outputs.html#resident-model-trip-file-final_tripscsv","title":"Resident Model trip file (final_trips.csv)","text":"Field Description trip_id Trip ID person_id Person ID household_id Household ID primary_purpose Primary purpose of tour (see tour table) trip_num Sequential number of trip by direction (1\u2026n where n is maximum trips on half-tour, e.g. max stops + 1) outbound TRUE if trip is in the outbound direction, else FALSE destination MGRA of trip destination origin MGRA of trip origin tour_id Tour ID escort_participants Space delimited string field listing person IDs of other children escorted on this trip, else null school_escort_direction String field indicating whether child is being dropped off at school (\u201coutbound\u201d) or picked up from school (\u201cinbound\u201d). \u201cnull\u201d if not a child being picked up or dropped off. purpose Purpose at destination destination_logsum Logsum from trip destination choice model. -9 if destination is tour origin or primary destination. depart Departure time period (1\u202648) trip_mode Trip mode string mode_choice_logsum Logsum from trip mode choice model vot Value of time for trip in dollars per hour ($2023) owns_transponder True if household owns transponder. Same as ownTrp totalWaitSingleTNC Wait time for single pay TNC totalWaitSharedTNC Wait time for shared\\pooled TNC s2_time_skims HOV2 travel time s2_dist_skims HOV3 travel distance s2_cost_skims HOV2 travel toll cost cost_parking Parking costs at trip origin and destination, calculated as one-half of the costs at each end, with subsidies considered. cost_fare_drive Taxi/TNC fare for any trip or trip portion taken on these modes distance_walk Distance walked on a trip (including access/egress for transit modes) time_mm Micromobility time distance_mm Micromobility distance cost_fare_mm Micromobility cost ($2023) distance_bike Bike distance time_wait_drive Waiting time for a TNC/ Taxi modes parking_zone MGRA from parking location choice model at destination, else -1 trip_period A string indicating the skim period for the trip (\u201cEA\u201d,\u201dAM\u201d,\u201dMD\u201d,\u201dPM\u2019,\u201dEV\u201d) tour_participants Number of joint tour participants if joint tour, else 1 distance_total Trip distance in miles cost_operating_drive Auto operating cost ($2023) weight_trip Trip weight defined as the ratio of number of particpants on a trip to the assumed occupancy rate of a mode (SHARED2,3) weight_person_trip Person trip weigth defined as the ratio of the number of particpants on a trip to sample rate of the model run inbound TRUE if trip is in the inbound direction, else FALSE time_drive Auto time distance_drive Auto distance cost_toll_drive Auto toll cost ($2023) time_transit_in_vehicle Transit in-vehicle time time_rapid_transit_in_vehicle Rapid transit in-vehicle time time_express_bus_transit_in_vehicle Express bus in-vehicle time time_local_bus_transit_in_vehicle Local bus in-vehicle time time_light_rail_transit_in_vehicle Light rail transit in-vehicle time time_commuter_rail_transit_in_vehicle Commuter rail in-vehicle time time_transit_initial_wait Transit initial-wait time cost_fare_transit Transit fare before subsidy ($2023) transfers_transit Number of transfers time_bike Bike time time_walk Walk mode time cost_total total cost of a trip (sum of auto operating, toll, transit fare) time_total Total time (sum of drive, bike, walk, initial transit wait, transit time, transit transfer)) time_transit_wait Total transit wait time (initial, transfer, NEV wait, waiting for school bus) value_of_time_category_id Value of time bin. 1: Low, 2: Medium, 3: High origin_micro_prm_dist Distance from trip origin MGRA to closest premium transit stop by microtransit dest_micro_prm_dist Distance from trip destination MGRA to closest premium transit stop by microtransit microtransit_orig Distance from trip origin MGRA to closest local transit stop by microtransit microtransit_dest Distance from trip destination MGRA to closest local transit stop by microtransit microtransit_available TRUE if microtransit is available for trip, else FALSE nev_orig Availability of Neighborhood Electric vehicle at origin nev_dest Availability of Neighborhood Electric vehicle at destination nev_available TRUE if neighborhood electric vehicle is available, else FALSE microtransit_access_available_out TRUE if microtransit is available from the origin, else FALSE nev_access_available_out TRUE if neighborhood electric vehicle is available from the origin, else FALSE microtransit_egress_available_out Availability of microtransit egress in the outbound direction nev_egress_available_out Availability of NEV egress in the outbound direction microtransit_access_available_in Availability of microtransit access in the inbound direction nev_access_available_in Availability of NEV egress in the inbound direction microtransit_egress_available_in Availability of microtransit egress in the inbound direction nev_egress_available_in Availability of microtransit egress in the outbound direction trip_veh_body Body type of vehicle used for trip, else \u201cnull\u201d trip_veh_age Age of vehicle used for trip, else \u201cnull\u201d trip_veh_fueltype Fuel type of vehicle used for trip, else \u201cnull\u201d origin_purpose Purpose at origin sample_rate Sample rate origin_parking_zone MGRA from parking location choice model at trip origin, else -1 otaz Origin TAZ dtaz Destination TAZ"},{"location":"outputs.html#resident-model-tour-mode-definitions","title":"Resident Model tour mode definitions","text":"Field Description DRIVEALONE Drive alone SHARED2 Shared ride with 2 participants SHARED3 Shared ride with 3+ participants WALK Walk BIKE Bike WALK_LOC Local transit with walk access/egress mode WALK_PRM Premium transit with walk access/egress mode WALK_MIX Mix (local with premium transfers) transit with walk access/egress mode PNR_LOC Local transit with Park&ride access or egress mode PNR_PRM Premium transit with Park&ride access or egress mode PNR_MIX Mix (local with premium transfers) transit with Park&ride access or egress mode KNR_LOC Local transit with Kiss&ride access or egress mode KNR_PRM Premium transit with Kiss&ride access or egress mode KNR_MIX Mix (local with premium transfers) transit with Kiss&ride access or egress mode TNC_LOC Local transit with TNC access or egress mode TNC_PRM Premium transit with TNC access or egress mode TNC_MIX Mix (local with premium transfers) transit with TNC access or egress mode TAXI Taxi TNC_SINGLE Private TNC ride TNC_SHARED Shared TNC ride SCH_BUS School bus EBIKE E-bike ESCOOTER E-scooter"},{"location":"outputs.html#resident-model-auto-demand-matrices","title":"Resident Model auto demand matrices","text":"Table Name Description SOVNOTRPDR_<> Drive Alone Non-Transponder for <> SOVTRPDR_<> Drive Alone Transponder for <> SR2NOTRPDR_<> Shared Ride 2 Non-Transponder for <> SR2TRPDR_<> Shared Ride 2 Transponder for <> SR3NOTRPDR_<> Shared Ride 3 Non-Transponder for <> SR3TRPDR_<> Shared Ride 3 Transponder for <>"},{"location":"outputs.html#resident-model-transit-demand-matrices","title":"Resident Model transit demand matrices","text":"Table Name Description <transit_class>_GENCOST__<> Total generalized cost which includes perception factors from assignment <transit_class>_FIRSTWAIT__<> actual wait time at initial boarding point <transit_class>_XFERWAIT__<> actual wait time at all transfer boarding points <transit_class>_TOTALWAIT__<> total actual wait time <transit_class>_FARE__<> fare paid <transit_class>_XFERS__<> number of transfers <transit_class>_ACCWALK__<> access actual walk time prior to initial boarding <transit_class>_EGRWALK__<> egress actual walk time after final alighting <transit_class>_TOTALWALK__<> total actual walk time <transit_class>_TOTALIVTT__<> Total actual in-vehicle travel time <transit_class>_DWELLTIME__<> Total dwell time at stops <transit_class>_BUSIVTT__<> actual in-vehicle travel time on local bus mode <transit_class>_LRTIVTT__<> actual in-vehicle travel time on LRT mode <transit_class> _CMRIVTT__<> actual in-vehicle travel time on commuter rail mode <transit_class> _EXPIVTT__<> actual in-vehicle travel time on express bus mode <transit_class>_LTDEXPIVTT__<> actual in-vehicle travel time on premium bus mode <transit_class>_BRTIVTT__<> actual in-vehicle travel time on BRT mode *time period = EA, AM, MD, PM, EV transit_class = BUS, ALLPEN, PREM"},{"location":"outputs.html#resident-model-non-motorized-demand-matrices","title":"Resident Model non-motorized demand matrices","text":"Table Name Description WALK_<> Walk for <> BIKE_<> Bike for <>"},{"location":"outputs.html#visitor-model-outputs-visitor","title":"Visitor model outputs (.\\visitor)","text":"
This directory contains outputs from the overnight visitor model.
File Description [final_households.csv](#### Visitor Model household file (final_households.csv)) Visitor model household file final_land_use.csv Visitor model land-use file [final_persons.csv](#### Visitor Model person file (final_persons.csv)) Visitor model person file [final_tours.csv](#### Visitor Model tour file (final_tours.csv)) Visitor model tour file [final_trips.csv](#### Visitor Model trip file (final_trips.csv)) Visitor model trip file model_metadata.yaml Visitor model Datalake metadata file nmotVisitortrips_(period).omx Visitor model non-motorized trips by period (EA, AM, MD, PM, EV) autoVisitortrips_(period).omx Visitor model auto trips by period (EA, AM, MD, PM, EV) transVisitortrips_(period).omx Visitor model transit trips by period (EA, AM, MD, PM, EV)"},{"location":"outputs.html#visitor-model-household-file-final_householdscsv","title":"Visitor Model household file (final_households.csv)","text":"Field Description home_zone_id Home MGRA sample_rate Sample rate household_id Household ID poverty Poverty indicator utilized for social equity reports. Percentage value where value <= 2 (200% of the Federal Poverty Level) indicates household is classified under poverty."},{"location":"outputs.html#visitor-model-person-file-final_personscsv","title":"Visitor Model person file (final_persons.csv)","text":"Field Description household_id Household ID home_zone_id Home MGRA person_id Person ID"},{"location":"outputs.html#visitor-model-tour-file-final_tourscsv","title":"Visitor Model tour file (final_tours.csv)","text":"Field Description tour_id Tour ID tour_type Type of tour. String. \"dining\", \"recreation\", or \"work\" purpose_id Type of tour. 0: work, 1: \"dining, 2: \"recreation\" visitor_travel_type Visitor purpose. String. \"business\" or \"personal\" tour_category Tour category. All tour categories in the visitor model are \"non-mandatory\" number_of_participants Number of participants on tour auto_available Auto availability indicator 0: not available, 1: available income Income 0 - 4 origin Tour origin MGRA tour_num Sequential number of tour 1 to n where n is total number of tours tour_count Number of tours per person household_id Household ID person_id Person ID start Half-hour time period of departure from tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. end Half-hour time period of arrival back at tour origin. Periods are number 1 through 48 where period 1 starts at 3:00 AM. duration Duration of the tour in number of half-hour periods, including all activity episodes and travel destination Tour primary destination MGRA destination_logsum Tour destination choice logsum tour_mode Tour mode mode_choice_logsum Tour mode choice logsum stop_frequency Number of stops on tour by direction. String. xout_yin where x is number of stops in the outbound direction and y is the number of stops in the inbound direction primary_purpose Primary purpose of a tour. String (recreation, dining, work)"},{"location":"outputs.html#visitor-model-trip-file-final_tripscsv","title":"Visitor Model trip file ((final_trips.csv)","text":"Field Description trip_id Trip ID person_id Person ID household_id Household ID primary_purpose Purpose at primary destination of tour. String. \"dining\", \"recreation\", or \"work\" trip_num Sequential number of trip on half-tour from 1 to 4 outbound TRUE if outbound, else FALSE trip_count Number of trips in a tour destination Destination MGRA origin Origin MGRA tour_id Tour ID purpose Destination purpose. String. \"dining\", \"recreation\", or \"work\" destination_logsum Destination choice logsum depart Departure time period (1\u202648) trip_mode Trip mode (see trip mode table) trip_mode_choice_logsum Mode choice logsum for trip vot_da will drop vot_s2 will drop vot_s3 will drop parking_cost Parking costs at trip origin and destination, calculated as one-half of the costs at each end, with subsidies considered. tnc_single_wait_time Wait time for single pay TNC tnc_shared_wait_time Wait time for shared\\pooled TNC taxi_wait_time Wait time for taxi cost_parking Cost of parking ($2023) cost_fare_drive Taxi/TNC fare for any trip or trip portion taken on these modes distance_walk Distance walked on a trip (including access/egress for transit modes) time_mm Micromobility time distance_mm Micromobility distance cost_fare_mm Micromobility cost ($2023) distance_bike Bike distance time_wait_drive Ridehail/Taxi wait times for a trip trip_period A string indicating the skim period for the trip (\u201cEA\u201d,\u201dAM\u201d,\u201dMD\u201d,\u201dPM\u2019,\u201dEV\u201d) tour_participants Number of tour participants distance_total Trip distance cost_operating_drive Auto operating cost ($2023) weight_trip Trip weight defined as the ratio of number of particpants on a trip to the assumed occupancy rate of a mode (SHARED2,3) weight_person_trip Person trip weigth defined as the ratio of the number of particpants on a trip to sample rate of the model run vot Value of time in dollars per hour ($2023) inbound TRUE if trip is in outbound direction, else FALSE time_drive Auto time distance_drive Auto distance cost_toll_drive Auto toll cost ($2023) time_transit_in_vehicle Transit in-vehicle time time_rapid_transit_in_vehicle Rapid transit in-vehicle time time_express_bus_transit_in_vehicle Express bus in-vehicle time time_local_bus_transit_in_vehicle Local bus in-vehicle time time_light_rail_transit_in_vehicle Light rail transit in-vehicle time time_commuter_rail_transit_in_vehicle Commuter rail in-vehicle time time_transit_initial_wait Transit initial-wait time cost_fare_transit Transit fare ($2023) transfers_transit Number of transfers time_bike Bike time time_walk Walk mode time cost_total total cost of a trip (sum of auto operating, toll, transit fare) time_total Total travel time (including iIVT and access/egress and wait times for all modes) value_of_time_category_id Value of time bin. 1: Low, 2: Medium, 3: High origin_micro_prm_dist Distance from trip origin MGRA to closest premium transit stop by microtransit dest_micro_prm_dist Distance from trip destination MGRA to closest premium transit stop by microtransit microtransit_orig Distance from trip origin MGRA to closest local transit stop by microtransit microtransit_dest Distance from trip destination MGRA to closest local transit stop by microtransit microtransit_available TRUE if microtransit is available for trip, else FALSE nev_orig True if Neghoborhood Electric Vehicle is available at origin nev_dest True if Neghoborhood Electric Vehicle is available at destination nev_available TRUE if neighborhood electric vehicle is available, else FALSE microtransit_access_available_out TRUE if microtransit is available from the origin, else FALSE nev_access_available_out TRUE if neighborhood electric vehicle is available from the origin, else FALSE microtransit_egress_available_out Availability of microtransit egress in the outbound direction nev_egress_available_out Availability of NEV egress in the outbound direction microtransit_access_available_in Availability of microtransit access in the inbound direction nev_access_available_in Availability of NEV egress in the inbound direction microtransit_egress_available_in Availability of microtransit egress in the inbound direction nev_egress_available_in Availability of microtransit egress in the outbound direction sample_rate Sample rate otaz Origin TAZ dtaz Destination TAZ"},{"location":"outputs.html#visitor-models-tour-mode-choice-definitions","title":"Visitor model\u2019s tour mode choice definitions","text":"Field Description DRIVEALONE Drive alone SHARED2 Shared ride with 2 participants SHARED3 Shared ride with 3+ participants WALK Walk BIKE Bike WALK_LOC Local transit with walk access/egress mode WALK_PRM Premium transit with walk access/egress mode WALK_MIX Mix (local with premium transfers) transit with walk access/egress mode TAXI Taxi TNC_SINGLE Private TNC ride TNC_SHARED Shared TNC ride"},{"location":"outputs.html#trip-mode-definitions","title":"Trip Mode Definitions","text":"Field Description DRIVEALONE Drive alone SHARED2 Shared ride with 2 participants SHARED3 Shared ride with 3+ participants WALK Walk BIKE Bike WALK_LOC Local transit with walk access/egress mode WALK_PRM Premium transit with walk access/egress mode WALK_MIX Mix (local with premium transfers) transit with walk access/egress mode PNR_LOC Local transit with Park&ride access or egress mode PNR_PRM Premium transit with Park&ride access or egress mode PNR_MIX Mix (local with premium transfers) transit with Park&ride access or egress mode KNR_LOC Local transit with Kiss&ride access or egress mode KNR_PRM Premium transit with Kiss&ride access or egress mode KNR_MIX Mix (local with premium transfers) transit with Kiss&ride access or egress mode TNC_LOC Local transit with TNC access or egress mode TNC_PRM Premium transit with TNC access or egress mode TNC_MIX Mix (local with premium transfers) transit with TNC access or egress mode TAXI Taxi TNC_SINGLE Private TNC ride TNC_SHARED Shared TNC ride SCH_BUS School bus EBIKE E-bike ESCOOTER E-scooter"},{"location":"release-notes.html","title":"Release Notes","text":""},{"location":"release-notes.html#version-1510-september-4-2024","title":"Version 15.1.0 (September 4, 2024)","text":"
As mentioned in the notes for Version 15.0.2, several improvements to the Commercial Vehicle Model (CVM) were made, largely due to SANDAG staff realizing that the survey used to estimate the CVM had likely overestimated the amount of commercial vehicle travel that was made on a given day in the region. New weights were estimated, and then the CVM was recalibrated to match these new weights. After doing this, it was found that modeled highway volumes were lower than observed counts, so some further adjustments were made to get them back up. Some components of the resident model were recalibrated to better match the survey, a new database started being used, a bug in the transit network was fixed, and other miscelaneous improvements were made.
PR 167, PR 172, & PR 173: Recalibration of the Commercial Vehicle Model to reflect newly-calculated Commercial Vehicle Survey weights.
PR 182, PR 186, PR 194, & PR 195: Miscellaneous calibration, particularly to counter the decrease in freeway travel caused by the CVM update.
PR 176 & PR 177: A new database was created for Version 15.1.0 and necessary scripts were modified to point to the new database. Go here to see detailed changes in the database and here for changes in the reporting tool.
PR 184, PR 186, & PR 193: The number of EV chargers each year were updated using numbers given by planning, and recalibration was done to ensure that the EV ownership rates for no build scenarios matched the targets.
PR 178, PR 188, & PR 189: In previous releases the crossborder model had its own constants.yaml file, which contained a lot of the same values in the common constants.yaml file, meaning that both files would need to be edited. The crossborder model now instead reads from the common constants.yaml file.
PR 179, PR 181, PR 190, PR 191, & PR 192: Miscellaneous updates to the scenario creation GUI and scenario build process.
PR 168, PR 170, PR 171, PR 175, & PR 185: A bug in the transit network where connectors were being encoded with the wrong units was fixed. This resulted in a sharp increase in the number of transit boardings so some additional calibration was needed to get them back down to observed levels.
PR 174 & PR 180: Miscellaneous file cleanup and restoration.
Since the release of version 15.0.1 more updates were made, particularly regarding the new commercial vehicle model. More updates to the CVM will be forthcoming due to ongoing revision based on new data sources.
For use in the 2025 Region Plan (RP), SANDAG has developed a new activity-based travel model, known as ABM3. The biggest change from SANDAG\u2019s previous ABM, ABM2+, is the transition from the Java-based CT-RAMP modeling platform to the open-source Python-based ActivitySim platform that has been developed by a consortium of public agencies (of which SANDAG is a founding member) for the past decade. ABM3 will be the first ActivitySim model to be used in production for planning purposes. While most of the model components from ABM3 were translated from ABM2+, there were several notable enhancements that were made, which are described below. Further, several models were either re-estimated and/or recalibrated to match 2022 data.
SANDAG/activitysim v15.0.0: This is based on a fork of the ActivitySim consortium repo and customized code in BayDAG_estimation branch.
SANDAG/activitysim-cvm v15.0.0: This specific version of ActivitySim is required to run the new Commercial Vehicle Model (CVM) in ABM3. It is based on a fork of the ActivitySim repo under CamSys and customized code in time-settings branch.
ABM PR 68: The base year was updated from 2016 to 2022, allowing the behavioral models to reflect changes in travel behavior derived from the onset of the COVID-19 pandemic.
ABM PR 65: The zone system was transitioned from a 3-zone system (TAZ, MAZ, TAP) to a 2-zone system (TAZ, MAZ).
ABM PR 66: E-bike and E-scooter were added as travel modes.
ABM PR 64: A new commercial vehicle model (CVM) was implemented using ActivitySim, that includes firm synthesis, commercial vehicle tours, and TNC services such as food delivery.
ABM PR 133: Network editing was changed from the ArcInfo-based TCOVED to TNED, a new network editing system.
ABM PR 104: Flexible fleet services were added for full trips along with first-mile access to and last-mile egress from fixed-route transit.
ABM PR 9: The airport model has been updated with new access options, including the proposed automated people-mover.
ABM PR 53: The cross-border model was updated, including a new wait time model.
Asim PR 635: A new disaggregate accessibility calculation step was introduced to improve the precision of accessibilities used by modeling components.
ABM PR 82: Transit assignment for different time periods is now run in parallel.
ABM Configs: The auto ownership model has been moved before work and school location.
Asim PR 613: The shadow pricing algorithm was updated to constrain mandatory tour locations to availability instead of traditional shadow pricing.
ABM PR 86: Model results are automatically written to a database stored in the cloud via Microsoft Azure Services.
"},{"location":"release-notes.html#new-resident-model-components","title":"New Resident Model Components","text":"
Autonomous Vehicle Ownership
External Worker Identification
External Workplace Location
Transit Pass Subsidy
Transit Pass Ownership
Vehicle Type Choice
Telecommute Frequency
External Joint Tour Identification
External Joint Tour Location
External Nonmandatory Tour Identification
External Nonmandatory Tour Destination
Vehicle Allocation
"},{"location":"running.html","title":"Installing and Running ABM3","text":"
This page describes how to install and run ABM3, including hardware and software requirements. In general, a powerful server is required to run the model. The main software required for the model includes EMME, Python, and Java. EMME is a commercial transportation modeling platform that must be purchased separately and requires a computer with a Windows operating system. Python is an open-source cross-platform programming language that is currently one of the most popular programming languages. Python is the core language of ActivitySim. Java is an open-source programming language required for certain bespoke non-ActivitySim model components.
ABM3 runs on a Microsoft Windows workstation or server, with the minimum and recommended system specification as follows:
Minimum specification:
Operating System: 64-bit Windows 7, 64-bit Windows 8 (8.1), 64-bit Windows 10, 64-bit Windows Server 2019
Processor: 24-core CPU processor
Memory: 1 TB RAM
Disk space: 500 GB
Recommended specification:
Operating System: 64-bit Windows 10
Processor: Intel CPU Xeon Gold / AMD CPU Threadripper Pro (24+ cores)
Memory: 1 TB RAM
Disk space: 1000 GB
In general, a higher CPU core count and RAM will result in faster run times as long as ActivitySim is configured to utilize the additional processors and RAM.
Note that the model is unlikely to run on servers that have less than 1 TB of RAM, unless chunking is set to active and in explicit mode (requires an upcoming version of ActivitySim 1.3)
Three software applications, EMME, Python package manager Anaconda, and Java should be installed on the computer that will be used to run the model.
The ABM3 model system is an integrated model that is controlled by and primarily runs in the EMME transportation planning software platform. EMME is used for network assignment and creating transportation skims, and the model\u2019s Graphical User Interface (GUI). The software also provides functionality for viewing and editing highway and transit network files and viewing of matrix files. The Bentley Connect Edition software (the license manager for EMME) will need to be logged in and activated prior to running the model.
A Python package manager is software that creates an environment for an instance of Python. ActivitySim and related Python processes in the model are executed in an environment that is setup with a specific version of Python and specific library versions. This ensures that changes outside of the Python environment will not cause errors or change model results, and additionally ensure that the specific version of Python and specific libraries needed by the model do not cause errors or changes to other Python software installations on the server. The libraries needed by ActivitySim extend the base functionality of Python. Note that Anaconda requires a paid subscription for agencies larger than 200 users. To install Anaconda, follow the instructions here.
Java is required in order to create bicycle logsums, run the taxi/TNC routing model, and run the intra-household autonomous vehicle routing model. The model has been tested against Java version 8 (e.g. 1.8) but should run in later versions as well.
"},{"location":"running.html#installing-abm3-model","title":"Installing ABM3 Model","text":""},{"location":"running.html#setting-up-the-python-environments","title":"Setting up the Python environments","text":"
As noted above, User needs to install Anaconda on the machine they are working on, if it is not already installed. The following step is creating a specific environment to run ActivitySim. The environment is a configuration of Python that is for ActivitySim - this environment allows ActivitySim to use specific software libraries without interfering with the server\u2019s installed version of Python (if one exists, it is not required) and keeps other Python installations from interfering with ActivitySim.
To run ABM3, user needs to install two different python environments, one in Python 3, which will be used by all ActivitySim-based models, and one in Python 2, which is required as long as the EMME version in use still depends on Python 2, and is used to convert the omx rip tables out of the ActivitySim models. To set up these environments, use the following instruction from within the Anaconda 3 PowerShell Prompt for Python 3 and Anaconda 2 PowerShell Prompt for Python 2.
To set up the Python 3 environment, first, change directories using cd /d to the environment folder under the ActivtySim source code directory. As of June 2024, this directory may be cloned from the BayDAG_estimation branch located on the SANDAG\u2019s forked version of ActivitySim here. The environment folder in this directory contains a number of yaml files that may be used to install the environment. User may use the following command to install the AcitvitySim environment along with SANDAG\u2019s version of AcitivtySim under the asim_baydag name.
After installing the environment, do a quick test of it by activating it, using:
conda activate asim_baydag
To set up the Python 2 environment, user simply needs to install the openmatrix package in the base environment. To do so, first open the Anaconda 2 terminal and use the following command to install the openmatrix package:
Java version 1.81 needs to be installed on the server. SANDAG servers usually have this version of Java already installed on them.
"},{"location":"running.html#creating-a-scenario-folder","title":"Creating a scenario folder","text":"
Follow the steps below to create a model scenario folder using SANDAG\u2019s tool:
Check for an open server using the DOS tool mascot-abm.
Log into server
Using the show hidden icons arrow on the taskbar, click the INRO Software Manager to check if EMME license is operating. If it\u2019s not, log into license server on Mustang by typing in the Host box: mustang.sandag.org:5170 (automatic)
Using the server\u2019s Windows Explorer, navigate to T:\\ABM\\release\\ABM\\version_14_2_2\\dist
Double click on createStudyAndScenario.exe and click Run.
On pop-up, enter fields in Create an ABM scenario. Make sure to enter the correct ABM version, EMME version, year, and know your Scenario Folder (where you will run the model from; typically study area/abm_runs) and your Network Folder (input networks). Browse to an existing folder or copy (ctrl + c) and paste (ctrl + v) paths to minimize chance of typos. After fields are entered, click Create.
A DOS window will open and necessary files will be copied. If you have a server with a valid license, it will pause for about 30-45 seconds on init emme folder. You will get a pop-up window that says you have successfully created the scenario. Click Quit. Exit the GUI by clicking on Quit.
Go to your newly created abm_run folder and double check on input files/date stamps to verify they match expected date stamps. Make any modifications to input files (i.e. parametersByYears.csv for adjusting auto operating costs, filesByYears.csv for specifying year-specific files, or mgra_based_inputXXX.csv file for adjusting parking costs)
To open the EMME application from the created scenario directory, user needs to go to the emme_project folder, and open the start_emme_with_virtualenv.bat file. This opens up the EMME application, where the application prompts the choice of a scenario. It is recommended to select the main highway scenario (Scen. 100) to start off the model run, although other scenarios may be selected as well.
Following this step, user should open the EMME Modeler by clicking on the gold square sign at the top left of the screen.
EMME Modeler icon
The EMME Modeler opens to the EMME Standard Toolbox, but needs to be switched to the SANDAG toolbox by selecting it from the bottom-left of the screen. From this toolbox, open the Master Run tool.
Opening SANDAG Toolbox and Master run
Opening the Master Run tool allows the user to run all or part of the model, and set a number of settings such as sample size.
Master Run tool
The Master run tool operates the SANDAG travel demand model. To operate the model, configure the inputs by providing Scenario ID, Scenario title and Emmebank title, and keeping Number of Processors to default. Select main ABM directory will automatically be set to the current project directory and does not require change.
Main ABM directory: directory which contains the ABM scenario data, including this project. The default is the parent directory of the current Emme project.
Scenario ID: Scenario ID for the base imported network data. The result scenarios are indexed in the next five scenarios by time period.
Scenario title: title to use for the scenario.
Emmebank title: title to use for the Emmebank (Emme database)
Number of processors: the number of processors to use for traffic and transit assignments and skims, aggregate demand models (where required) and other parallelized procedures in Emme. Default is Max available \u2013 1.
Properties (conf/sandag_abm.properties)
On opening the Master run tool the sandag_abm.properties file is read and the values cached and the inputs below are pre-set. When the Run button is clicked this file is written out with the values specified. Any manual changes to the file in-between opening the tool and clicking the Run button are overwritten.
Sample rate by iteration: three comma-separated values for the ActivitySim sample rates for each iteration
Start from iteration: start iteration for the model run
Skip steps: optional checkboxes to skip model steps.
Select link: add select link analyses for traffic.
By expanding the Run model \u2013 skip steps drop down, the user can make any custom changes. Usually the defaults should be sufficient although if you are using a new bike network, you should uncheck the Skip bike logsums and check the Skip copy of bikelogsum.
Run model tool
Following this setup, you can click Run to start the model run. We recommend occasionally checking the model run status to make sure the run is going smoothly. When the model run finishes successfully, the Master Run tool will show a model run successful message in green at the top of the tool window.
If the run is unsuccessful (there will be an error prompt from Emme), check Emme logbook and log files (under \u201clogfiles\u201d) for clues to where it stopped.
As the model runs, a full runtime trace of the model steps, inputs and reports is recorded in the Modeller Logbook. As each step completes, it will record an entry in the Logbook along with reports and other results. The Logbook can be opened from the Clock-like icon in the upper right of the Modeller window. This icon can also be found in the toolbar in the Emme Desktop. If a Modeller tool is running, a window will pop-up over the Emme Desktop which includes a Show Logbook button (this window can be closed to use Desktop worksheets and tables while any tool is running). Click on the Refresh button to update the logbook view with the latest status.
Modeller logbook
The Logbook provides a real time, automated documentation of the model execution. The overall structure of the model is represented at the top level, with the occurrence, sequence and repetition of the steps involved in the model process. Nested Logbook entries may be collapsed or expanded to see detail. For the Emme assignment procedures, interactive charts are recorded. The statistical summaries of the impedance matrices are recorded for each time period following the assignment. These summary tables provide an easy way to check for skims with obvious outlier values.
"},{"location":"application/applying.html","title":"Applying the model","text":"
This page contains information needed to apply the model.
One of the policies that SANDAG planners would like to test for the 2025 Regional Plan is providing rebates for low- and middle-income households to purchase electric vehicles. One of the variables in the vehicle type choice model is the new purchase price for a vehicle of a given age, body type, and fuel type. The way the EV rebate is implemented in ABM3 is by deducting the appropriate rebate value for plugin and battery vehicles if a household meets the criteria (based on percentage of the federal poverty level). To configure the rebate values and poverty level thresholds, new constants were added to the common/constants.yaml configuration file. The constants fit into the policy as follows:
The of the five big moves defined in SANDAG\u2019s 2021 regional plan was Flexible Fleets, which involves on-demand transit services. The initial concept of the 2025 Regional Plan involves rapidly expanding these services, with many new services planned to be in operation by 2035. For this reason, it is important that these services be modeled by ABM3. There are two flavors of flexible fleets that were incorporated into ABM3, Neighborhood Electric Vehicles (NEV) and microtransit. A table contrasting these services is shown below.
Characteristic NEV Microtransit Vehicle Size Smaller Larger Service Area Smaller Larger Operating Speed Slower Faster"},{"location":"application/flexible-fleets.html#incorporation-into-abm3","title":"Incorporation into ABM3","text":"
Rather than creating new modes for flexible fleet services, microtransit and NEV were incorporated into existing modes. How this was done was dependent on whether the trip was a full flexible fleet trip, first-mile access to fixed-route transit, or last-mile egress from fixed-route transit. A table explaining how each of these trip types was incorporated into ABM3 is shown below. Further, a heirarchy of services is enforced. ActivitySim first checks if NEV is available (based on a new land use attribute), and if it is, it\u2019s assumed that NEV is used. If not, ActivitySim checks if microtransit is available (based on a corresponding land use attribute), and if it is, it\u2019s assumed that microtransit is used. If neither are available, ActivitySim looks at the other services that are already available.
*For trips on the return leg of a tour the access and egress attributes are swapped
Full microtransit trip First-mile access to fixed-route transit Last-mile egress from fixed-route transit What models allow for this type of trip? Resident, Visitor, Crossborder Resident Resident, Visitor, Crossborder Which mode is used? TNC Shared TNC to transit All transit modes How is the flexible fleet travel time factored into the trip? The travel time is the full travel time of the trip The travel time is added to the transit access time and a transfer is added The travel time is added to the transit egress time and a transfer is added if the destination is further from the nearest transit stop than a user would be willing to walk (that distance is configurable) How is the flexible fleet cost factored into the trip? The cost is the full cost of the trip It is assumed that flexible fleet services are free when used to access fixed-route transit It is assumed that flexible fleet services are free when egressing from fixed-route transit"},{"location":"application/flexible-fleets.html#new-attributes","title":"New Attributes","text":"
Several new attributes were added to allow the user to configure how flexible fleet services are operated. These are all defined in the common constants.yaml file. Each attribute is defined as follow:
Attribute Definition Default value Speed Assumed operating speed in miles per hour MT: 30, NEV: 17 Cost Cost of using service in US Cents 125 for both WaitTime Assumed time passengers wait to wait to use service in minutes 12 for both MaxDist Maximum distance in miles that the service can be used MT: 4.5, NEV: 3 DiversionConstant Additional travel time to divert for servicing other passengers 6 for both DiversionFactor Time multiplier accounting for diversion to service other passengers 1.25 for both StartPeriod Time period to start service (not yet implemented) 9 for both EndPeriod Time period to end service (not yet implemented) MT: 32, NEV: 38 maxWalkIfMTAccessAvailable Maximum disatance someone is willing to walk at the destination end if flexible fleet services are available (same for microtransit and NEV) 1.0"},{"location":"application/flexible-fleets.html#travel-time-calculation","title":"Travel Time Calculation","text":""},{"location":"application/flexible-fleets.html#direct-time","title":"Direct Time","text":"
The flexible fleet travel time calculation is a two-step process. The first step is to calculate the time that it would take to travel from the origin to the destination* directly without any diversion to pick up or drop off any passengers. This is done by taking the maximum of the time implied by the operating speed and the congested travel time:
\\(d = \\textnormal{Distance from origin to destination (taken from distance skim)}\\)
\\(t_{\\textnormal{congested}} = \\textnormal{Congested travel time from origin to destination (taken from Shared Ride 3 time skim)}\\)
*When used to access fixed-route transit, the destination is the nearest transit stop to the trip origin. When used to egress from fixed-route transit, the origin is the nearest transit stop to the trip destination.
The second step of the travel time calculation was to account for diversion to pick up other passengers. These were based on guidelines used in a NEV pilot. The formula to calculated the total flexible fleet travel time is as follows:
ABM3 uses a python module as the scenario manager. The job of this scenario manager is updating the parameters used throughout the model to match a specific scenario\u2019s definition and needs. A number of these parameters including auto operating cost, taxi and TNC fare, micromobility cost, and AV ownership penetration are usually assumed to change by forecast year or scenario.
Manually changing these parameters requires the model user to know where each parameter is located, and individually changing them according to the scenario forecast values. A scenario manager, therefore, can be a convenient and efficient tool to automate this process.
The ABM3 Scenario Manager reads in a CSV input file (located under input/parametersByYears.csv) containing the parameter values for each scenario, and updates the associated parameters in the ActivitySim config files. A snapshot of this input parameter CSV file is shown below, where each row is associated with a specific scenario year/name. The parameter names used here can either be identical to the parameter names used in ActivitySim, or different. In case the parameter names are different, a separate file is used to map the parameters names between the input CSV and ActivitySim config files.
The scenario manager is run as part of the model setup in the Master Run tool before any ActivitySim model is run (usually only in the first iteration of the run). Model user can choose to run or skip this step, although it is highly recommended to run with each run to ensure correct parameters.
The ABM3 model system is primarily based on the ActivitySim platform; ActivitySim is used to model resident travel, cross-border travel, overnight visitor travel, airport ground access travel, and commercial vehicle travel including light, medium, and heavy commercial vehicles. Aggregate models are used to model external-internal travel (from external stations other than the U.S./Mexico border crossing) and through travel. The model system relies on EMME software for network processing, skimming, and assignment. Models are mostly implemented in Python, and some models are implemented in [Java] (https://www.java.com/en/).
The overall design of the model is shown in the figure below.
The system starts by performing initial input processing in EMME. This includes building transport networks and scenarios for skimming and assignment. An initial set of skims are created based on input trip tables (e.g. warm start). Then disaggregate choice models in ActivitySIm are run, including the resident model, the crossborder travel model, two airport ground access models, the overnight visitor model, and the commercial vehicle model. Next auxiliary models are run; the taxi/TNC routing model and the autonomous vehicle intra-household allocation model are run in Java. Aggregate external-internal and through travel models are run in Python. After all models are run, trip tables are built from the result and assigned to transport networks. A check is made to determine whether the model has reached convergence (currently this is set to three feedback iterations). If convergence is reached, outputs are processed for export to the SANDAG Datalake for reporting summaries. If not, speeds from assignment are averaged using method of successive averages, and skims are rebuilt for the next iteration. The model system is then re-run with the updated skims.
ActivitySim is used to represent all internal travel and internal-external made by residents of the SANDAG region (modeled area). The decision-makers in the model system include both persons and households. These decision-makers are created (synthesized) for each simulation year and land-use scenario, based on Census data and forecasted distributions of households and persons by key socio-economic categories. A similar but simplified method is used to generate disaggregate populations for cross-border, airport ground access, and overnight visitor models. The decision-makers are used in the subsequent discrete-choice models in a microsimulation framework where a single alternative is selected from a list of available alternatives according to a probability distribution. The probability distribution is generated from a logit model which considers the attributes of the decision-maker and the attributes of the various alternatives. The application paradigm is referred to as Monte Carlo simulation, since a random number draw is used to select an alternative from the probability distribution. The decision-making unit is an important element of model estimation and implementation and is explicitly identified for each model specified in the following sections.
A key advantage of using the micro-simulation approach is that there are essentially no computational constraints on the number of explanatory variables that can be included in a model specification. However, even with this flexibility, the model system will include some segmentation of decision-makers. Segmentation is a useful tool to both structure models (for example, each person type segment could have their own model for certain choices) and to characterize person roles within a household. Segments can be created for persons as well as households.
There are two airport ground access models - one for San Diego International Airport and one for the Crossborder Express terminal which provides access to Tijuana International Airport from the United States. Both models use the same structure and software code, though the parameters that control the total number of airport travel parties, off-airport destination, mode, arrival and departure times, and other characteristics, vary for each airport according to survey and airport-specific data. The airport ground access model simulates trips to and from the airport for residents, visitors, and external travelers. These trips are generated by arriving or departing passengers and are modeled as tours within the ActivitySim framework. A post processing script also generates trips to serve passengers who require a pickup or dropoff at the airport. For example, a passenger who is picked up at the airport generates two trips; one trip to the airport by the driver to pick up the air passenger(s), and another trip from the airport with the driver and the air passenger(s). It is important to note that, to work within the ActivitySim framework, the airport trips must be modeled as tours, rather than being generated directly as in the previous model. These tours are assigned an origin at the airport MGRA. During the stop frequency step of ActivitySim, a trip is assigned to the appropriate leg of the tour (either to or from the airport) while the opposite leg is not assigned any trips (referred to as the \u2018dummy leg\u2019). Passengers who are leaving on a departing flight and traveling to the airport are considered \u201cinbound,\u201d while arriving passengers are considered \u201coutbound\u201d.
The overall design of the model is shown in the figure below.
Tour Enumeration: A list of airport travel parties is generated from input enplanements and transferring passenger rates, as well as distributions that describe the share of travel parties by purpose (business versus personal), household income, and party size.
Tour Level Models
2.1 Tour Scheduling Probabilistic: The tour scheduling model uses a probabilistic draw of the scheduling distribution. This model assigns start and end times to the tour. This is important because it will also serve as the schedule model for the final airport trips. In ActivitySim, trips are scheduled based on the tour schedule. If there is only one trip per leg on the tour (such as our case here) the trip is assigned the tour start/end time.
2.2 Tour Destination Choice: The destination choice model chooses the non-airport end of the airport trips. Each tour is set with an origin at the airport MGRA. The tour destination model of ActivitySim is used to choose the non-airport end of the trip. The utility equation includes the travel distance, and the destination size terms. ActivitySim destination choice framework requires a mode choice log sum. A dummy tour mode choice log sum was created which generates a value of zero for every destination using the \u2018tour_mode_choice.csv\u2019 and \u2018tour_mode_choice.yml\u2019 file. This is a work around to prevent ActivitySim from crashing and not having to include the tour mode choice log sum in the destination choice model.
2.3 Stop Frequency Choice: The stop frequency model is where the trip table is first created. The pre-processor tags each tour with a direction of \u2018inbound\u2019 or \u2018outbound\u2019 according to whether the tour is a departing or arriving passenger. For the Airport Ground Access model, inbound tours are tagged with zero outbound trips and -1 inbound trips (and the opposite is true for outbound tours: -1 outbound trips and 0 inbound trips). The 0 signifies that no intermediate stops are made; this leg of the tour will only have one trip. The -1 signifies that no trip is made at all on that leg. Using the -1 allows us to create \u2018half-tours\u2019 where only one leg of the tour is recorded as a trip. 3. Trip Level Models
3.1 Trip Departure Choice: The trip scheduling model assigns depart times for each trip on a tour. ActivitySim requires trip scheduling probabilities; however, these are not used in this implementation since there is only one trip on any given tour leg. This means the trips will be assigned the tour scheduling times which were determined in the tour scheduling model. The trip scheduling probabilities file is just a dummy file.
3.2 Trip Mode Choice: Each trip is assigned a trip mode; in the Airport Ground Access Model, trip mode refers to the airport arrival mode which simultaneously predicts the arrival mode and the location which the passenger uses to access that model. The arrival modes are shown in the table below. The trip mode choice yaml file contains detailed variables associated with each trip mode. For example, each parking location is given an MGRA location, a walk time, a wait-time, and a cost. If a parking location MGRA is set to -999 it is assumed to be unavailable and will not be in the choice set. The pre-processor in this step stores all values of skims from the trip origin to each of the access modes destinations along with any associated costs. Costs include parking fees per day, access fees, fares, and rental car charges. Employees are not fed into the trip mode choice model. Instead, if a transit share is specified in the employee park file, that percentage of employees will be assigned \u2018Walk Premium\u2019 mode in the pre-processor. Otherwise, employees are all assigned \u2018Walk\u2019 mode from the employee parking lot to the terminal.
3.3 Airport Returns: Airport trips where the party is dropped of curbside or parked and escorted are assumed to also have the driver make a return trip to the non-airport location. This procedure is done as a post-processing step after mode choice and before trip tables are written out. An \u2018airport_returns.yml\u2019 file takes a user setting to determine which trip modes will include a return trip. These trips records are flagged and duplicated. The duplicated trips swap the origin and destination of the original trip and are assigned a unique trip id. These trips are tagged with \u2018trip_num =2\u2019 so they are easily sorted in any additional processing (such as for writing trip matrices).
3.4 Write trip matrices: The write trip matrices step converts the trip lists into vehicle trip matrices. The matrices are segmented by trip mode and value of time bins. The vehicle trip modes in the matrices include SOV, HOV2, HOV3+, Taxi, and TNC-single. Value of time segmentation is either low, medium, or high bins based on the thresholds set in the model settings.
The major tour modes are shown below:
flowchart TD\n subgraph four\n direction LR\n E[Ride-Hail] --> E1[Taxi]\n end\n subgraph three\n direction LR\n D[Transit] --> D1[Walk Access]\n %% D --> D2[PNR Access]\n D --> D3[KNR Access]\n D --> D4[TNC Access]\n\n D1 --> D11[Local Only]\n D1 --> D12[Premium Only]\n D1 --> D13[Mixed]\n\n D3 --> D31[Local Only]\n D3 --> D32[Premium Only]\n D3 --> D33[Mixed]\n\n D4 --> D41[Local Only]\n D4 --> D42[Premium Only]\n D4 --> D43[Mixed]\n end\n subgraph two\n direction LR\n C[Active] --> C1[Walk]\n end\n\n subgraph one\n direction LR\n B[Auto] --> B1[Drive-alone];\n B --> B2[Shared 2];\n B --> B3[Shared 3+];\n end\n A[Tour Mode] --> one;\n A --> two;\n A --> three;\n A --> four;\n\n classDef group1 fill:#f75f5f,stroke:#333,stroke-width:4px,font-size:26px,font-weight:bold;wrap\n classDef group2 fill:#ffd966,stroke:#333,stroke-width:2px,font-size:20px;wrap\n classDef group3 fill:#bdabf0,stroke:#333,stroke-width:2px,font-size:18px;wrap\n\n classDef hiddenTitle color:transparent;\n class one,two,three,four hiddenTitle;\n\n class A group1;\n class B,C,D,E group2;\n class B1,B2,B3,C1,C2,C3,C4,D1,D2,D3,D4,E1,E2,E3,D11,D12,D13,D21,D22,D23,D31,D32,D33,D41,D42,D43 group3;\n
"},{"location":"design/demand/airport.html#airport-ground-access-model-trip-arrival-modes","title":"Airport Ground Access Model Trip Arrival Modes","text":"Arrival Mode Description Park Location 1 Party parks personal vehicle at parking location 1. Park Location 2 Party parks personal vehicle at parking location 2. Park Location 3 Party parks personal vehicle at parking location 3. Park Location 4 Party parks personal vehicle at parking location 4. Park Location 5 Party parks personal vehicle at parking location 5. Curb Location 1 Party is dropped off or picked up by another driver at curbside location 1. Curb Location 2 Party is dropped off or picked up by another driver at curbside location 2. Curb Location 3 Party is dropped off or picked up by another driver at curbside location 3. Curb Location 4 Party is dropped off or picked up by another driver at curbside location 4. Curb Location 5 Party is dropped off or picked up by another driver at curbside location 5 Park and Escort Party is driven in personal vehicle, parks on-site at the airport and is escorted to/from airport. Rental Car Party arrives/departs by rental car. Shuttle Van Party takes shuttle van. Hotel Courtesy Party takes hotel courtesy transportation. Ridehail Location 1 Party arrives\\departs using ridehail at ridehail location 1 Ridehail Location 2 Party arrives\\departs using ridehail at ridehail location 2 Taxi Location 1 Party arrives\\departs using taxi at taxi location 1 Taxi Location 2 Party arrives\\departs using taxi at taxi location 2 Walk Local Party arrives\\departs using walk-local bus Walk Premium Party arrives\\departs using walk-premium transit Walk Mix Party arrives\\departs using walk-local plus premium transit KNR Local Party arrives\\departs using KNR-local bus KNR Premium Party arrives\\departs using KNR-premium transit KNR Mix Party arrives\\departs using KNR-local plus premium transit TNC Local Party arrives\\departs using TNC-local bus TNC Premium Party arrives\\departs using TNC-premium transit TNC Mix Party arrives\\departs using TNC-local plus premium transit Walk Party arrives\\departs using walk
For more information on the Air Ground Access Travel Model see technical documentation.
The Cross-Border Travel Model predicts travel made by residents of Mexico within San Diego County. It predicts the border crossing point of entry as well as all trips made within the county. The model is limited to simulating travel made by Mexican residents who return to Mexico within the simulation day. Cross-border travel not captured by the Cross-Border Model includes:
Residents of San Diego County who travel to/from Mexico. This travel is represented by the resident travel model.
Residents of Mexico (including U.S. citizens) who travel into San Diego County and who do not return to Mexico at the end of the day. This travel is represented in the overnight visitor travel model.
Travel between points of entry through San Diego County to other U.S. destinations. This travel is represented by the external-external travel model.
Commercial vehicle travel to/from points of entry. This travel is represented by the commercial vehicle model.
The overall design of the model is shown in the figure below.
"},{"location":"design/demand/crossborder.html#crossborder-model-purpose-definitions","title":"Crossborder Model Purpose Definitions","text":"
There are five activity purposes in the cross-border travel demand model: * Work: Any activity involving work for pay. * School: Pre-k school, K-12, college/university, or trade school. * Shop: Shopping at retail, wholesale, etc. * Visit: Visiting friends or family * Other: A broad category including eating out, medical appointments, recreational activities, etc.
Note that home activities are not listed, since we do not model activities south of the border.
"},{"location":"design/demand/crossborder.html#crossborder-model-mode-definitions","title":"Crossborder Model Mode Definitions","text":"
The major tour modes are shown below:
flowchart TD\n subgraph three\n direction LR\n D[Transit] --> D1[Walk Access]\n\n D1 --> D11[Local Only]\n D1 --> D12[Premium Only]\n D1 --> D13[Mixed]\n end\n subgraph two\n direction LR\n C[Active] --> C1[Walk]\n end\n\n subgraph one\n direction LR\n B[Auto] --> B1[Drive-alone];\n B --> B2[Shared 2];\n B --> B3[Shared 3+];\n end\n A[Tour Mode\\Border Crossing Mode] --> one;\n A --> two;\n A --> three;\n\n classDef group1 fill:#f75f5f,stroke:#333,stroke-width:4px,font-size:26px,font-weight:bold;wrap\n classDef group2 fill:#ffd966,stroke:#333,stroke-width:2px,font-size:20px;wrap\n classDef group3 fill:#bdabf0,stroke:#333,stroke-width:2px,font-size:18px;wrap\n\n classDef hiddenTitle color:transparent;\n class one,two,three hiddenTitle;\n\n class A group1;\n class B,C,D,E group2;\n class B1,B2,B3,C1,C2,C3,C4,D1,D11,D12,D13 group3;\n
The model has the following mode types at the trip level: * Drive-alone: Single occupant private vehicle * Shared 2: A private vehicle with exactly two passengers * Shared 3+: A private vehicle with three or more passengers * Walk: Walk mode * Bike: Bike mode * Walk-transit: Walk access to transit. There are three sub-types of transit: Local only, premium only, local + premium (which includes both local and premium services in the transit path) * Taxi: Door-to-door taxi trip * Single-pay TNC: Door-to-door TNC trip with a single payer (e.g. UberX) * Shared-pay TNC: Stop-to-stop TNC trip with potentially multiple payers (e.g. UberPool)
We also model tour mode, which is the mode used to cross the border. These modes include drive-alone, shared 2, shared 3+ and walk. We assume that anyone crossing by bus or taxi is similar to walk, since they do not have access to a personal vehicle for the rest of their travel in San Diego County.
We also classify border crossings by lane type: general purpose, SENTRI, and Ready. We assume that the use of these lanes is related to the border crossing party; we attribute each party with SENTRI or Ready availability. The proportion of total border crossing parties with access to SENTRI and Ready lanes are based on observed survey data, pooled across all stations. This data is used to simulate the availability of the lane to the travel party. Each lane crossing type is related to the wait time that the travel party experiences at each border crossing station by mode.
Below is a general description of the model structure.
Tour Enumeration: A list of person-tours is created by first cross-multiplying the input total person tours with the share of tours by pass type, then expanding tours by pass type to tours by pass type and purpose.
Tour Level Models 2.1 Time-of-day Choice: Each person-tour is assigned an outbound and return half-hour period.
2.2 Primary Destination and Station Choice: Each border crossing person-tour chooses a primary destination MGRA and border crossing station.
2.3 Border Crossing Mode Choice: Each person-tour chooses a border crossing tour mode.
Wait Time Model
3.1. Wait time model: Calculate wait time based on demand at each POE from model 2.2
3.2. Convergence check: If max iterations reached (currently 3), goto Stop and Trip level models, else goto Model 2.2 3. Stop and Trip Level Models 4.1 Stop Frequency Choice: Each person-tour is assigned number of stops by half-tour (outbound, return).
4.2 Stop Purpose Choice: Each stop is assigned a stop purpose (consistent with the tour purposes).
4.3 Trip Departure Choice: Each trip is assigned a half-hourly time period.
4.4 Stop Location Choice: Each stop chooses an MGRA location.
4.5 Trip Mode Choice: Each trip is assigned a trip mode.
4.6 Trip Assignments: Trips are assigned to networks, along with resident and other special market trip tables, and skims are created for the next iteration of the model.
For more information on the Crossborder Travel Model see technical documentation.
The external aggregate travel models predict characteristics of US-SD and SD-US/MX travel behavior for all non-commercial, non-visitor vehicle trips and selected transit trips. Note that non-commercial MX-SD trips are forecast in the crossborder model, and non-commercial SD-US and SD-MX trips are forecast in the resident model.
"},{"location":"design/demand/external.html#external-model-estimation-of-trip-counts-by-type","title":"External Model Estimation of Trip Counts by Type","text":"
The total count of trips by production and attraction location was estimated in a series of steps:
The number of crossborder trips made by Mexican residents to attractions in San Diego was previously determined.
The trips in the resident travel survey were expanded to estimate the total number of trips made by San Diego residents to attractions in Mexico.
To derive an estimate of the number of US-MX trips, the number of MX-SD (1) and SD-MX (2) trips was subtracted from the total number of border-crossings.
The distribution of US-MX trips among external stations on the US-side of San Diego County will be assumed to be proportional to the total volume at each external station, regardless of the point of entry at the Mexican border.
The number of US-MX trips was then subtracted from the total number of trips in the SCAG cordon survey to arrive at an estimate of the combined total of US-US, US-SD, and SD-US trips with routes through San Diego County.
Finally, the actual amounts of US-US, US-SD, and SD-US trips at each external station were estimated from the remaining trips (4) according to their proportions in the successfully geocoded responses in the SCAG cordon survey.
"},{"location":"design/demand/external.html#external-model-design-overview","title":"External Model Design Overview","text":"
The behavioral characteristics of the different types of external trip were derived from the various data sources available as follows:
US-US trips: a fixed external station OD trip matrix was estimated from the SCAG cordon survey.
/src/main/emme/toolbox/model/external_external.py
US-MX trips: a fixed external station OD trip matrix was estimated from the SCAG cordon survey, Customs and Border Protection vehicle counts, and Mexican resident border-crossing survey as described in the previous section.
/src/main/emme/toolbox/model/external_external.py
US-SD trips: rates of vehicle trips per household for each external county were developed from the SCAG cordon survey, and the trips were distributed to locations in San Diego County according to a destination choice model estimated from the interregional survey.
Intermediate stops and transit trips are not modeled in this segment due to the small contribution of these events to the total demand in the segment.
The external-internal destination choice model distributes the EI trips to destinations within San Diego County. The EI destination choice model explanatory variables are:
Distance
The size of each sampled MGRA
Diurnal and vehicle occupancy factors are then applied to the total daily trip tables to distribute the trips among shared ride modes and different times of day.
The trips are then split among toll and non-toll paths according to a simplified toll choice model. The toll choice model included the following explanatory variables:
The resident model structure is based on the Coordinated Travel Regional Activity-based Modeling Platform (CT-RAMP). The figure below shows the resident model structure. In order to understand the flow chart, some definitions are required. These are described in more detail below.
Tour: A sequence of trips that start and end at an anchor location. In ActivitySim, anchors are home or work.
Primary destination: The \u201cmain\u201d activity of the tour; this activity determines the tour purpose. It also divides the tour into two \u201clegs\u201d; the sequence of trips from the anchor location to the primary destination is the outbound leg, and the sequence of trips from the primary destination back to the anchor location is the inbound or return leg.
Mandatory activity: Work or school
Non-mandatory activity: Any out of home activity that is not work or school, including maintenance activities such as shopping as well as discretionary activities such as out-of-home recreation and eating out.
Fully joint tour: A tour in which two or more household members travel together to all out-of-home activity locations and return home together. In other words, no household member is picked-up or dropped-off en route.
Intermediate stop: An out-of-home activity location on the tour other than the anchor location or the primary destination. Intermediate stops are made on the way from the anchor location to the primary destination (outbound) or on the way from the primary destination back to the anchor location (inbound).
Tour mode: The \u201cmain mode\u201d or \u201cpreferred mode\u201d of the tour. This is an abstract concept used categorize the tour with respect to accessibility and constrain the availability of modes for trips on the tour to ensure some consistency of modes used for each trip.
The resident model design is shown below.
The first model in the sequence is disaggregate accessibilities. This is a recent addition to ActivitySim in which the tour destination choice model is run for a prototypical sample population covering key market segments and destination choice logsums from the model are written out for each tour in the population. These destination choice logsums are then merged with the actual synthetic population and used as accessibility variables in downstream models such as auto ownership, coordinated daily activity patterns, and tour frequency. are mandatory location choice; this model is run for all workers and students regardless of whether they attend work or school on the simulated day.
Next a set of long-term and mobility models are run. The first model in the sequence predicts whether an autonomous vehicle is owned by the household. This model conditions the next model, which predicts the number of autos owned. If an autonomous vehicle is owned, multiple cars are less likely. Next, the mandatory (work and school) location choice models are run. The work location choice models includes a model to predict whether the worker has a usual out-of-home work location or exclusively works from home. If the worker chooses to work from home, they will not generate a work tour. An external worker identification model determines whether each worker with an out-of-home workplace location works within the region or external to the region. If they work external to the region, the external station is identified. Any primary destination of any work tours generated by the worker will be the external station chosen by this model. A work location choice model predicts the internal work location of each internal worker, and a school location choice model predicts the school location of each student.
Next, a set of models predicts whether workers and students have subsidized transit fares and if so, the percent of transit fare that is subsidized, and whether each person in the household owns a transit pass. A vehicle type choice model then runs, which predicts the body type, fuel type, and age of each vehicle owned by the household; this model was extended to predict whether each vehicle is autonomous, conditioned by the autonomous vehicle ownership model.
Next, we predict whether each household has access to a vehicle transponder which can be used for managed lane use. We assume that all vehicles built after a certain year (configurable by the user) are equipped with transponders. Next we predict whether each worker has subsidized parking available at work. Finally we predict the telecommute frequency of each worker, which affects downstream models including the daily activity pattern model, the non-mandatory tour frequency model, and stop frequency models.
Next the daily and tour level models are run. The first daily model is the daily activity pattern model is run, which predicts the general activity pattern type for every household member. Then Mandatory tours are generated for workers and students, the tours are scheduled (their location is already predicted by the work/school location choice model), a vehicle availability model is run that predicts which household vehicle would be used for the tour, and the tour mode is chosen. After mandatory tours are generated, a school pickup/dropoff model forms half-tours where children are dropped off and/or picked up at school. The model assigns chaperones to drive or ride with children, groups children together into \u201cbundles\u201d for ride-sharing, and assigns the chaperone task to either a generated work tour or generates a new tour for the purpose of ridesharing. Fully joint tours \u2013 tours where two or more household members travel together for the entire tour - are generated at a household level, their composition is predicted (adults, children or both), the participants are determined, the vehicle availability model is run, and a tour mode is chosen. The primary destination of fully joint tours is predicted, the tours are scheduled, the vehicle availability model is run, and a tour mode is chosen. Next, non-mandatory tours are generated, their primary destination is chosen, they are scheduled, the vehicle availability model is run, and a tour mode is chosen for each. At-work subtours are tours that start and end at the workplace. These are generated, scheduled (with constraints that the start and end times must nest within the start and end time of the parent work tour), a primary destination is selected, the vehicle availability model is run, and a tour mode is chosen.
The major tour modes are shown below:
flowchart TD\n subgraph four\n direction LR\n E[Ride-Hail] --> E1[Taxi]\n E --> E2[Single-pay TNC]\n E --> E3[Shared TNC];\n end\n subgraph three\n direction LR\n D[Transit] --> D1[Walk Access]\n D --> D2[PNR Access]\n D --> D3[KNR Access]\n D --> D4[TNC Access]\n\n D1 --> D11[Local Only]\n D1 --> D12[Premium Only]\n D1 --> D13[Mixed]\n\n D2 --> D21[Local Only]\n D2 --> D22[Premium Only]\n D2 --> D23[Mixed]\n\n D3 --> D31[Local Only]\n D3 --> D32[Premium Only]\n D3 --> D33[Mixed]\n\n D4 --> D41[Local Only]\n D4 --> D42[Premium Only]\n D4 --> D43[Mixed]\n end\n subgraph two\n direction LR\n C[Active] --> C1[Walk]\n C --> C2[Bike]\n C --> C3[E-Scooter]\n C --> C4[E-Bike]\n end\n\n subgraph one\n direction LR\n B[Auto] --> B1[Drive-alone];\n B --> B2[Shared 2];\n B --> B3[Shared 3+];\n end\n A[Tour Mode] --> one;\n A --> two;\n A --> three;\n A --> four;\n\n classDef group1 fill:#f75f5f,stroke:#333,stroke-width:4px,font-size:26px,font-weight:bold;wrap\n classDef group2 fill:#ffd966,stroke:#333,stroke-width:2px,font-size:20px;wrap\n classDef group3 fill:#bdabf0,stroke:#333,stroke-width:2px,font-size:18px;wrap\n\n classDef hiddenTitle color:transparent;\n class one,two,three,four hiddenTitle;\n\n class A group1;\n class B,C,D,E group2;\n class B1,B2,B3,C1,C2,C3,C4,D1,D2,D3,D4,E1,E2,E3,D11,D12,D13,D21,D22,D23,D31,D32,D33,D41,D42,D43 group3;\n
At this point, all tours are generated, scheduled, have a primary destination, and a selected tour mode. The next set of models fills in details about the tours - number of intermediate stops, location of each stop, the departure time of each stop, and the mode of each trip on the tour. Finally, the parking location of each auto trip to the central business district (CBD) is determined. After the model is run, the output files listed above are created. The trip lists are then summarized into origin-destination matrices by time period and vehicle class or transit mode and assigned to the transport network.
The Overnight Visitor Model simulates trips of visitors staying overnight in hotels, motels, short-term vacation rentals, and with friends and family. The trips are modeled as part of tours that begin and end at the place of lodging. However, unlike the resident model, the Overnight Visitor Model does not utilize a 24-hour activity schedule. Therefore it can be thought of as a simpler, tour-based model. Once each tour is generated, it is scheduled independently. The model uses the same time periods and modes as the resident model. The overall design of the model is shown in the figure below.
Tour Enumeration: A list of visitor parties is generated from the input household data and hotel room inventory at the MGRA level. Visitor travel parties by segment (business versus personal) are calculated based on separate rates for hotels and for households. Visitor parties are generated by purpose based on tour rates by segment, then attributed with household income and party size based on input distributions. There are three purposes in the Overnight Visitor model:
Work: Business travel made by business visitors.
Dining: Travel to food establishments for both business and personal visitors.
Recreational: All other non-work non-food related activities.
Tour Level Models
2.1 Tour Scheduling Probabilistic: The tour scheduling model uses a probabilistic draw of the scheduling distribution. This model assigns start and end times to the tour. If there is only one trip per leg on the tour, the trip is assigned the tour start/end time.
2.2 Tour Destination Choice: The destination choice model chooses the MGRA of the primary activity location on the tour.
2.3 Tour Mode Choice: The tour mode choice model determines the primary mode of the tour.
Stop Level Models
3.1 Stop Frequency Choice: The stop frequency model predicts the number of stops by direction based on input distributions that vary by tour purpose.
3.2 Stop Purpose: The stop purpose model chooses the activity purpose of each intermediate stop based on input distributions that vary according to tour purpose.
3.3 Stop Location Choice: The location choice model chooses the MGRA for each intermediate stop on the tour.
Trip Level Models
4.1 Trip Departure Choice: The trip scheduling model assigns depart times for each trip on a tour based on input distributions that vary by direction (inbound versus outbound), stop number, and number of periods remaining on the tour.
4.2 Trip Mode Choice: Each trip is assigned a trip mode, consistent with the modes in the resident model.
4.3 Trip Assignment: Trips are aggregated by time of day, mode occupancy, value-of-time, and origin-destination TAZ and assigned simultaneously with other trips.
The major tour modes are shown below:
flowchart TD\n subgraph four\n direction LR\n E[Ride-Hail] --> E1[Taxi]\n E --> E2[Single-pay TNC];\n end\n subgraph three\n direction LR\n D[Transit] --> D1[Walk Access]\n\n D1 --> D11[Local Only]\n D1 --> D12[Premium Only]\n D1 --> D13[Mixed]\n end\n subgraph two\n direction LR\n C[Active] --> C1[Walk]\n end\n\n subgraph one\n direction LR\n B[Auto] --> B1[Drive-alone];\n B --> B2[Shared 2];\n B --> B3[Shared 3+];\n end\n A[Tour Mode] --> one;\n A --> two;\n A --> three;\n A --> four;\n\n classDef group1 fill:#f75f5f,stroke:#333,stroke-width:4px,font-size:26px,font-weight:bold;wrap\n classDef group2 fill:#ffd966,stroke:#333,stroke-width:2px,font-size:20px;wrap\n classDef group3 fill:#bdabf0,stroke:#333,stroke-width:2px,font-size:18px;wrap\n\n classDef hiddenTitle color:transparent;\n class one,two,three,four hiddenTitle;\n\n class A group1;\n class B,C,D,E group2;\n class B1,B2,B3,C1,C2,C3,C4,D1,D2,D3,D4,E1,E2,E3,D11,D12,D13,D21,D22,D23,D31,D32,D33,D41,D42,D43 group3;\n\n
For more information on the Overnight Visitor Travel Model see technical documentation.
Model outputs are written to the data lake immediately following ABM3 model run completion.
Output CSV files are converted to Parquet format before writing to the data lake.
Each model run is assigned a unique scenario ID.
Data lake files are loaded to Delta tables:
Each output file in the data lake is loaded into its corresponding Delta table. For example, the trips output file is loaded into the trips Delta table, the persons output file is loaded into the persons Delta table, etc.
Delta tables store the results from all model runs, organized by scenario ID.
Delta Tables are processed in Databricks:
Delta tables are read, transformed, and aggregated as needed to support analysis and reporting requirements.
Once transformations are complete, the resulting data is written back to the data lake as new Delta tables or used to update existing tables.
These new Delta tables are also organized by scenario ID, making it easier to manage and query specific versions of processed data.
Delta tables are ingested by Power BI:
Power BI reads the data from the Delta tables.
Power BI report templates with various metrics of interest are automatically refreshed with new model run outputs.
Metrics can easily be compared across different scenario IDs.
"},{"location":"design/supply/highway-skims-assign.html","title":"Highway Skimming and Assignment","text":"
Details of highway skimming and assignment.
"},{"location":"design/supply/network-import-tned.html","title":"Network Import from TNED","text":"
This section describes the procedure by which the ABM3 model system imports (into Emme) network (highway and transit) files along with a general description of the different network files.
The ABM3 model system has been configured to be compatible with SANDAG\u2019s Transportation Network Editing Database (TNED) system, which is utilized to edit, maintain and generate transportation networks. The TNED network files, generated via an ETL (i.e., Extract, Tranform, Load) procedure, serve as inputs to the ABM3 model system\u2019s import network procedure and are produced in text file, shapefile, geodatabase table and geodatabase feature class geodatabase formats. There are, additionally, some non-TNED input network files which are manually maintained.
The following are the required network files used during the Emme import network procedure:
File Source Description EMMEOutputs.gdb/TNED_HwyNet TNED Roadway network links EMMEOutputs.gdb/TNED_HwyNodes TNED Roadway network nodes EMMEOutputs.gdb/TNED_RailNet TNED Rail network links EMMEOutputs.gdb/TNED_RailNodes TNED Rail network nodes EMMEOutputs.gdb/Turns TNED Turn prohibition records special_fares.txt Manually Maintained Special fares in terms of boarding and incremental in-vehicle costs timexfer_{time_of_day}.csv Manually Maintained Timed transfer pairs of lines, by period. Where time_of_day refers to EA, AM, MD, PM, or EV. trrt.csv TNED Attribute data (modes, headways) for the transit lines trlink.csv TNED Sequence of links (routing) for the transit lines trstop.csv TNED Stop data for the transit lines MODE5TOD.csv Manually Maintained Global (per-mode) transit cost and perception attributes vehicle_class_toll_factors.csv Manually Maintained Factors to adjust the toll cost by facility name and class"},{"location":"design/supply/network-import-tned.html#import-network-procedure","title":"Import Network Procedure","text":"
This section describes the main steps carried out during the Emme import network procedure. The entire process is executed by the import_network.py script.
This step creates the different combinations of traffic and transit modes that will get applied to the network links. A mode defines a group of vehicles or users which have access to the same parts of the network. Modes are used in both the traffic and transit assignments to define the available network for each class of demand. Each mode is uniquely identified by a single case-sensitive character. The modes which have access to a given link are listed on that link, and each link must allow at least one mode.
"},{"location":"design/supply/network-import-tned.html#create-roadway-base-network","title":"Create Roadway Base Network","text":"
This step creates the base roadway network by importing it from the EMMEOutputs.gdb/TNED_HwyNet and EMMEOutputs.gdb/TNED_HwyNodes. The nodes and links (referred to as the base network in Emme) for the traffic network are imported from the TNED_HwyNode and TNED_HwyNet geodatabase feature classes. The nodes are created first and the links connect between them. The I-node (from node, field AN) and J-node (to node, field BN) are used to associate the nodes and links and uniquely identify the link in the Emme network. Separate forward (AB) and reverse (BA) links are generated for links that have been coded as two-way.
This step processes the EMMEOutputs.gdb/Turns input network file to generate turn restrictions by to- and from- link ID. If the indicated link IDs do not make a valid turn (links not adjacent) an error is reported.
This step calculates derived traffic attributes. It utilizes the vehicle_class_toll_factors.csv to adjust toll costs by facility name and class.
"},{"location":"design/supply/network-import-tned.html#check-zone-access","title":"Check Zone Access","text":"
This step verifies that every centroid has at least one available access and egress connector.
"},{"location":"design/supply/network-import-tned.html#create-rail-base-network","title":"Create Rail Base Network","text":"
This step creates the base roadway network by importing it from the EMMEOutputs.gdb/TNED_RailNet and EMMEOutputs.gdb/TNED_RailNodes. The nodes and links (referred to as the base network in Emme) for the rail network are imported from the TNED_RailNode and TNED_RailNet geodatabase feature classes. The nodes are created first and the links connect between them. The I-node (from node, field AN) and J-node (to node, field BN) are used to associate the nodes and links and uniquely identify the link in the Emme network. Separate forward (AB) and reverse (BA) links are generated for links that have been coded as two-way.
This step creates the transit lines by importing them from the trrt.csv, trlink.csv and trstop.csv input network files and matched to the transit base network. The mode-level attributes from MODE5TOD.csv, which vary by mode, are copied to transit line attributes and used in transit assignment. It is in this step also where the timexfer_{time_of_day}.csv files are used to explicitly set route-to-route specific transfer transit times.
The transit line and stop / segment attributes (including fares) are imported to Emme attributes. The special_fares.txt lists network-level incremental fares by boarding (line and/or stop) and in-vehicle segment. They specify additive fares based on the network elements encountered on a transit journey and are used to represent the Coaster (or other) zonal fare system.
"},{"location":"design/supply/transit-skims-assign.html","title":"Transit Skimming and Assignment","text":"
The transit assignment uses a headway-based approach, where the average headway between vehicle arrivals for each transit line is known, but not exact schedules. Passengers and vehicles arrive at stops randomly and passengers choose their travel itineraries considering the expected average waiting time.
The Emme Extended transit assignment is based on the concept of optimal strategy but extended to support a number of behavioral variants. The optimal strategy is a set of rules which define sequence(s) of walking links, boarding and alighting stops which produces the minimum expected travel time (generalized cost) to a destination. At each boarding point the strategy may include multiple possible attractive transit lines with different itineraries. A transit strategy will often be a tree of options, not just a single path. A line is considered attractive if it reduces the total expected travel time by its inclusion. The demand is assigned to the attractive lines in proportion to their relative frequencies.
The shortest \u201ctravel time\u201d is a generalized cost formulation, including perception factors (or weights) on the different travel time components, along with fares, and other costs / perception biases such as transfer penalties which vary over the network and transit journey.
The model has four access modes to transit (walk, park-and-ride (PNR), kiss-and-ride (KNR), and Transportation Network Company (TNC)) and three transit sets (local bus only, premium transit only, and local bus and premium transit sets), for 12 total demand classes by 5 TOD. These classes are assigned by slices, one at a time, to produce the total transit passenger flows on the network.
While there are 12 slices of demand, there are only three classes of skims: Local bus only, premium only, and all modes. The access mode does not change the assignment parameters or skims.
