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seattle-base.conf
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include "../common/akka.conf"
include "../common/metrics.conf"
include "../common/matsim.conf"
# This configuration is copied from beam-template.conf from https://github.com/LBNL-UCB-STI/beam
# The commented parameters keep default value from beam-template.conf to make it more readable
##################################################################
# EXPERIMENTAL
##################################################################
# beam.experimental.optimizer.enabled = "boolean | false"
##################################################################
# EXECUTION
##################################################################
# beam.cluster.enabled = "boolean | false"
# beam.actorSystemName = "string | ClusterSystem"
# beam.cluster.clusterType = "String? |"
# beam.useLocalWorker = "boolean | true"
##################################################################
# SIMULATION
##################################################################
beam.inputDirectory = "production/seattle"
# For subsequential beam runs (some data will be loaded from the latest found run in this directory)
# beam.input.lastBaseOutputDir = ${beam.outputs.baseOutputDirectory}
# This prefix is used to find the last run output directory within beam.input.lastBaseOutputDir directory
# beam.input.simulationPrefix = ${beam.agentsim.simulationName}
beam.agentsim.simulationName = "seattle-base"
# beam.agentsim.randomSeedForPopulationSampling = "int?"
beam.agentsim.agentSampleSizeAsFractionOfPopulation = 1.0
# beam.agentsim.fractionOfPlansWithSingleActivity = "double | 0.0"
# beam.agentsim.thresholdForWalkingInMeters = 100
# beam.agentsim.thresholdForMakingParkingChoiceInMeters = 100
beam.agentsim.schedulerParallelismWindow = 30
# beam.agentsim.timeBinSize = "int | 3600"
beam.agentsim.firstIteration = 0
beam.agentsim.lastIteration = 0
beam.agentsim.endTime = "60:00:00"
# beam.agentsim.scheduleMonitorTask.initialDelay = 1
# beam.agentsim.scheduleMonitorTask.interval = 30
beam.agentsim.snapLocationAndRemoveInvalidInputs = true
# beam.agentsim.agents.bodyType = "BODY-TYPE-DEFAULT"
# TERMINATION CRITERION NAME OPTIONS:
# TerminateAtFixedIterationNumber TerminateAtRideHailFleetStoredElectricityConvergence
# beam.sim.termination.criterionName = "TerminateAtFixedIterationNumber"
# beam.sim.termination.terminateAtRideHailFleetStoredElectricityConvergence.minLastIteration = "int | 0"
# beam.sim.termination.terminateAtRideHailFleetStoredElectricityConvergence.maxLastIteration = "int | 0"
# beam.sim.termination.terminateAtRideHailFleetStoredElectricityConvergence.relativeTolerance = "double | 0.01"
# MODE CHOICE OPTIONS:
# ModeChoiceMultinomialLogit ModeChoiceTransitIfAvailable ModeChoiceDriveIfAvailable ModeChoiceRideHailIfAvailable
# ModeChoiceUniformRandom ModeChoiceLCCM
# beam.agentsim.agents.modalBehaviors.modeChoiceClass = "ModeChoiceMultinomialLogit"
# beam.agentsim.agents.modalBehaviors.maximumNumberOfReplanningAttempts = 3
# beam.agentsim.agents.modalBehaviors.defaultValueOfTime = "double | 8.0"
# beam.agentsim.agents.modalBehaviors.minimumValueOfTime = "double | 7.25"
# The types have to be in sync with file pointed in the parameter `beam.agentsim.agents.vehicles.vehicleTypesFilePath`
# ["BUS-DEFAULT:1.0","RAIL-DEFAULT:1.0","FERRY-DEFAULT:1.0","SUBWAY-DEFAULT:1.0","CABLE_CAR-DEFAULT:1.0","TRAM-DEFAULT:1.0"]
beam.agentsim.agents.modalBehaviors.transitVehicleTypeVOTMultipliers = ["BUS-AC:2.0", "BUS-SF:2.0", "BUS-ST:2.0",
"BUS-VTA:2.0", "BUS-DEFAULT:2.0", "CABLE_CAR-SF:3.0", "BUS-VN:2.0", "SUBWAY-DEFAULT:0.75"]
# beam.agentsim.agents.modalBehaviors.modeVotMultiplier.transit = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.modeVotMultiplier.bike = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.modeVotMultiplier.walk = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.modeVotMultiplier.rideHail = "double | 1.0"
beam.agentsim.agents.modalBehaviors.modeVotMultiplier.rideHailPooled = 2.0
# beam.agentsim.agents.modalBehaviors.modeVotMultiplier.rideHailTransit = "double | 1.0"
beam.agentsim.agents.modalBehaviors.modeVotMultiplier.waiting = 3.0
# beam.agentsim.agents.modalBehaviors.modeVotMultiplier.CAV = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.modeVotMultiplier.drive = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.overrideAutomationLevel = "int | 1"
# beam.agentsim.agents.modalBehaviors.overrideAutomationForVOTT = "boolean | false"
# beam.agentsim.agents.modalBehaviors.poolingMultiplier.Level5 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.poolingMultiplier.Level4 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.poolingMultiplier.Level3 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.poolingMultiplier.LevelLE2 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.bikeMultiplier.commute.ageGT50 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.bikeMultiplier.noncommute.ageGT50 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.bikeMultiplier.commute.ageLE50 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.bikeMultiplier.noncommute.ageLE50 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.highCongestion.highwayFactor.Level5 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.highCongestion.nonHighwayFactor.Level5 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.lowCongestion.highwayFactor.Level5 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.lowCongestion.nonHighwayFactor.Level5 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.highCongestion.highwayFactor.Level5 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.highCongestion.nonHighwayFactor.Level5 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.lowCongestion.highwayFactor.Level5 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.lowCongestion.nonHighwayFactor.Level5 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.highCongestion.highwayFactor.Level4 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.highCongestion.nonHighwayFactor.Level4 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.lowCongestion.highwayFactor.Level4 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.lowCongestion.nonHighwayFactor.Level4 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.highCongestion.highwayFactor.Level4 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.highCongestion.nonHighwayFactor.Level4 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.lowCongestion.highwayFactor.Level4 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.lowCongestion.nonHighwayFactor.Level4 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.highCongestion.highwayFactor.Level3 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.highCongestion.nonHighwayFactor.Level3 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.lowCongestion.highwayFactor.Level3 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.lowCongestion.nonHighwayFactor.Level3 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.highCongestion.highwayFactor.Level3 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.highCongestion.nonHighwayFactor.Level3 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.lowCongestion.highwayFactor.Level3 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.lowCongestion.nonHighwayFactor.Level3 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.highCongestion.highwayFactor.LevelLE2 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.highCongestion.nonHighwayFactor.LevelLE2 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.lowCongestion.highwayFactor.LevelLE2 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.highTimeSensitivity.lowCongestion.nonHighwayFactor.LevelLE2 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.highCongestion.highwayFactor.LevelLE2 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.highCongestion.nonHighwayFactor.LevelLE2 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.lowCongestion.highwayFactor.LevelLE2 = "double | 1.0"
# beam.agentsim.agents.modalBehaviors.lowTimeSensitivity.lowCongestion.nonHighwayFactor.LevelLE2 = "double | 1.0"
beam.agentsim.agents.modalBehaviors.multinomialLogit.params.transfer = -1.4
# beam.agentsim.agents.modalBehaviors.multinomialLogit.params.transit_crowding = "double | 0.0"
# beam.agentsim.agents.modalBehaviors.multinomialLogit.params.transit_crowding_percentile = "double | 90.0"
# any positive value
# beam.agentsim.agents.modalBehaviors.multinomialLogit.params.transit_crowding_VOT_multiplier = "double | 0.0"
# from 0 to 1.
# how full should be a vehicle to turn on crowding calculation. 0 - empty, 1 - full
# beam.agentsim.agents.modalBehaviors.multinomialLogit.params.transit_crowding_VOT_threshold = "double | 0.5"
beam.agentsim.agents.modalBehaviors.multinomialLogit.params.car_intercept = 0.0
beam.agentsim.agents.modalBehaviors.multinomialLogit.params.cav_intercept = 0.0
beam.agentsim.agents.modalBehaviors.multinomialLogit.params.walk_transit_intercept = 0.0
# beam.agentsim.agents.modalBehaviors.multinomialLogit.params.drive_transit_intercept = "double | 0.0"
beam.agentsim.agents.modalBehaviors.multinomialLogit.params.ride_hail_transit_intercept = 0.0
beam.agentsim.agents.modalBehaviors.multinomialLogit.params.ride_hail_intercept = 0.0
beam.agentsim.agents.modalBehaviors.multinomialLogit.params.ride_hail_pooled_intercept = -1.5
beam.agentsim.agents.modalBehaviors.multinomialLogit.params.walk_intercept = 0.0
beam.agentsim.agents.modalBehaviors.multinomialLogit.params.bike_intercept = 0.0
# beam.agentsim.agents.modalBehaviors.multinomialLogit.params.bike_transit_intercept = "double | 0.0"
# beam.agentsim.agents.modalBehaviors.multinomialLogit.params.ride_hail_subscription = "double | 0.0"
# beam.agentsim.agents.modalBehaviors.multinomialLogit.utility_scale_factor = "double | 1.0"
beam.agentsim.agents.modalBehaviors.lccm.filePath = ${beam.inputDirectory}"/lccm-long.csv"
## SECONDARY ACTIVITIES GENERATION
# Allow/disallow generation of secondary activities
# beam.agentsim.agents.tripBehaviors.multinomialLogit.generate_secondary_activities = "boolean | false"
# Input file giving the relative likelihoods of starting different activities at different times of the day
# beam.agentsim.agents.tripBehaviors.multinomialLogit.intercept_file_path = ""
# Input file giving parameters for the different activity types, including mean duration (duration is drawn from an
# exponential distribution with that mean) and value of time multiplier. The value of time multiplier modifies how
# willing agents are to incur travel time and cost in order to accomplish that activity. For example, a value of 0.5
# means that they get 50% more value out of participating in that activity than they would being at home or work.
# So, if it's a 30 minute activity, they would on average be willing to spend 15 minutes round trip to participate in
# it. If the value is 2, they get 200% more value, so on average they would be willing to spend 60 minutes round trip
# commuting to participate in this activity.
#
# You can adjust the VOT values up or down to get more or less of a given activity
# beam.agentsim.agents.tripBehaviors.multinomialLogit.activity_file_path = ""
#This is an intercept value you can add to make all secondary activities more or less likely
# beam.agentsim.agents.tripBehaviors.multinomialLogit.additional_trip_utility = "double | 0.0"
# This sets a maximum distance in looking for places to participate in secondary activities. Increasing it
# increases the maximum and mean trip distance for secondary activities.
# beam.agentsim.agents.tripBehaviors.multinomialLogit.max_destination_distance_meters = "double | 32000"
# This determines how many options for secondary activity locations an agent chooses between. Increasing this
# number decreases the mean distance traveled to secondary activities and slightly increases the number of trips
# that are made (because the agents are more likely to find a suitable location for a secondary activity nearby)
# beam.agentsim.agents.tripBehaviors.multinomialLogit.max_destination_choice_set_size = "int | 20"
#These three values should all be between zero and one and determine the amount of noise in each level of
# the nested choice process. Increasing destination_nest_scale_factor means that people are more likely to
# choose a less optimal destination, mode_nest_scale_factor means people are more likely to value destinations
# accessible by multiple modes, and trip_nest_scale_factor means that people are more likely to take secondary
# trips even if the costs are greater than the benefits
# beam.agentsim.agents.tripBehaviors.multinomialLogit.destination_nest_scale_factor = "double | 1.0"
# beam.agentsim.agents.tripBehaviors.multinomialLogit.mode_nest_scale_factor = "double | 1.0"
# beam.agentsim.agents.tripBehaviors.multinomialLogit.trip_nest_scale_factor = "double | 1.0"
# beam.agentsim.agents.tripBehaviors.carUsage.minDistanceToTrainStop = "double | 0.0"
beam.agentsim.agents.modeIncentive.filePath = ${beam.inputDirectory}"/incentives.csv"
beam.agentsim.agents.ptFare.filePath = ${beam.inputDirectory}"/ptFares.csv"
# beam.agentsim.agents.parking.multinomialLogit.params.rangeAnxietyMultiplier = "double | -0.5" # fit range anxiety between [-1, 0]
# beam.agentsim.agents.parking.multinomialLogit.params.distanceMultiplier = "double | -0.086" # based on 20m walk, VoT = $35/hr should be u=-1.0
# beam.agentsim.agents.parking.multinomialLogit.params.parkingPriceMultiplier = "double | -0.005" # based on avg. parking prices up to $200!
# beam.agentsim.agents.parking.multinomialLogit.params.homeActivityPrefersResidentialParkingMultiplier = "double | 1.0" # positive boost of 1.0 if true
# beam.agentsim.agents.parking.multinomialLogit.params.enrouteDetourMultiplier = "double | -0.05" # based on skim travel time and the defined VoT from modalBehaviors and income
# beam.agentsim.agents.parking.rangeAnxietyBuffer = "double | 20000.0" # if our remaining range exceeds our remaining tour plus this many meters, then we feel no anxiety# default 20k
# beam.agentsim.agents.parking.minSearchRadius = "double | 250.00" # something small enough that, on average, we see 2 orders of magnitude of samples at most
# beam.agentsim.agents.parking.maxSearchRadius = "double | 8046.72" # something large enough that we do not strand our drivers in an oasis
# beam.agentsim.agents.parking.searchMaxDistanceRelativeToEllipseFoci = "double | 4.0" # something large enough that we do not travel longer than the actual distance to destination
# beam.agentsim.agents.parking.estimatedMinParkingDurationInSeconds = "double | 60.0" # in seconds, something small enough but has meaningful impact on pricing
# beam.agentsim.agents.parking.estimatedMeanEnRouteChargingDurationInSeconds = "double | 1800.0" # 80% of the battery charges in 30min
# beam.agentsim.agents.parking.fractionOfSameTypeZones = "double | 0.5"
# beam.agentsim.agents.parking.minNumberOfSameTypeZones = "int | 10"
# beam.agentsim.agents.parking.forceParkingType = "boolean | false"
#TAZ params (Can be TAZ or any other zoning system)
beam.agentsim.taz.filePath = ${beam.inputDirectory}"/shape/block-groups-32048.shp"
beam.agentsim.taz.tazIdFieldName = "OBJECTID"
beam.agentsim.taz.parkingFilePath = ${beam.inputDirectory}"/parking/taz-parking.csv.gz"
beam.agentsim.taz.parkingStallCountScalingFactor = ${beam.agentsim.agentSampleSizeAsFractionOfPopulation}
beam.agentsim.taz.parkingCostScalingFactor = ${beam.agentsim.agentSampleSizeAsFractionOfPopulation}
# options: DEFAULT, HIERARCHICAL, PARALLEL
# beam.agentsim.taz.parkingManager.method = "String | DEFAULT"
# beam.agentsim.taz.parkingManager.displayPerformanceTimings = false
# beam.agentsim.taz.parkingManager.parallel.numberOfClusters = "int | 8"
#Toll params
beam.agentsim.toll.filePath = ${beam.inputDirectory}"/toll-prices.csv"
# Ride Hailing Params: Options are ALL, MASS, or the individual modes comma separate, e.g. BUS,TRAM
# beam.agentsim.agents.rideHailTransit.modesToConsider = "MASS"
# Ride Hailing Params: Options are MIN_COST, MIN_UTILITY
# beam.agentsim.agents.rideHail.bestResponseType = "MIN_COST"
# Ride Hailing General Params
beam.agentsim.agents.rideHail.linkFleetStateAcrossIterations = true
# beam.agentsim.agents.rideHail.iterationStats.timeBinSizeInSec = "double | 3600.0"
# Ride Hailing SurgePricing parameters
# beam.agentsim.agents.rideHail.surgePricing.surgeLevelAdaptionStep = "double | 0.1"
# beam.agentsim.agents.rideHail.surgePricing.minimumSurgeLevel = "double | 0.1"
# beam.agentsim.agents.rideHail.surgePricing.priceAdjustmentStrategy = "KEEP_PRICE_LEVEL_FIXED_AT_ONE"
# beam.agentsim.agents.rideHail.surgePricing.numberOfCategories = "int | 6"
# Ride Hailing Managers Params
beam.agentsim.agents.rideHail.managers = [{
name = "Uber",
supportedModes="ride_hail, ride_hail_pooled"
initialization.initType = "PROCEDURAL" # Other possible values - FILE
initialization.filePath = ${beam.inputDirectory}"/rideHailFleet.csv.gz"
# initialization.parking.filePath = ""
initialization.procedural.vehicleTypeId = "Car"
initialization.procedural.vehicleTypePrefix = "RH"
initialization.procedural.fractionOfInitialVehicleFleet = 0.00287777
# HOME, RANDOM_ACTIVITY, UNIFORM_RANDOM, ALL_AT_CENTER, ALL_IN_CORNER
initialization.procedural.initialLocation.name = "RANDOM_ACTIVITY"
initialization.procedural.initialLocation.home.radiusInMeters = 500
initialization.procedural.vehicleAdjustmentMethod = "INCOME_BASED"
# stopFilePath = ""
maximumWalkDistanceToStopInM = 800.0
radiusInMeters = 5000
defaultBaseCost = 5.1
defaultCostPerMile = 1.12
defaultCostPerMinute = 0.32
pooledBaseCost = 4.47
pooledCostPerMile = 0.84
pooledCostPerMinute = 0.07
allocationManager.name = "POOLING_ALONSO_MORA"
allocationManager.maxWaitingTimeInSec = 1200
allocationManager.maxExcessRideTime = 0.5 # up to +50%
allocationManager.requestBufferTimeoutInSeconds = 300
# ASYNC_GREEDY_VEHICLE_CENTRIC_MATCHING, ALONSO_MORA_MATCHING_WITH_ASYNC_GREEDY_ASSIGNMENT, ALONSO_MORA_MATCHING_WITH_MIP_ASSIGNMENT
allocationManager.matchingAlgorithm = "ALONSO_MORA_MATCHING_WITH_ASYNC_GREEDY_ASSIGNMENT"
# ALONSO MORA
allocationManager.alonsoMora.maxRequestsPerVehicle = 5
# Reposition
allocationManager.pooledRideHailIntervalAsMultipleOfSoloRideHail = 1
# DEFAULT_REPOSITIONING_MANAGER, DEMAND_FOLLOWING_REPOSITIONING_MANAGER, INVERSE_SQUARE_DISTANCE_REPOSITIONING_FACTOR, REPOSITIONING_LOW_WAITING_TIMES, THE_SAME_LOCATION_REPOSITIONING_MANAGER, ALWAYS_BE_REPOSITIONING_MANAGER
repositioningManager.name = "INVERSE_SQUARE_DISTANCE_REPOSITIONING_FACTOR"
repositioningManager.timeout = 300
# Larger value increase probability of the ride-hail vehicle to reposition
# repositioningManager.demandFollowingRepositioningManager.sensitivityOfRepositioningToDemand = "double | 1"
# repositioningManager.demandFollowingRepositioningManager.sensitivityOfRepositioningToDemandForCAVs = "double | 1"
# repositioningManager.demandFollowingRepositioningManager.numberOfClustersForDemand = "int | 30"
# repositioningManager.demandFollowingRepositioningManager.fractionOfClosestClustersToConsider = "double | 0.2"
# repositioningManager.demandFollowingRepositioningManager.horizon = "int | 1200"
# inverse Square Distance Repositioning Factor
repositioningManager.inverseSquareDistanceRepositioningFactor.sensitivityOfRepositioningToDemand = 0.2
repositioningManager.inverseSquareDistanceRepositioningFactor.sensitivityOfRepositioningToDistance = 0.88668
repositioningManager.inverseSquareDistanceRepositioningFactor.predictionHorizon = 3600
# reposition Low Waiting Times
# allocationManager.repositionLowWaitingTimes.repositionCircleRadiusInMeters = "double | 3000"
# allocationManager.repositionLowWaitingTimes.minimumNumberOfIdlingVehiclesThresholdForRepositioning = "int | 1"
# allocationManager.repositionLowWaitingTimes.percentageOfVehiclesToReposition = "double | 0.01"
# allocationManager.repositionLowWaitingTimes.timeWindowSizeInSecForDecidingAboutRepositioning = "double | 1200"
# allocationManager.repositionLowWaitingTimes.allowIncreasingRadiusIfDemandInRadiusLow = true
# allocationManager.repositionLowWaitingTimes.minDemandPercentageInRadius = "double | 0.1"
# allocationManager.repositionLowWaitingTimes.repositioningMethod = "TOP_SCORES"
# allocationManager.repositionLowWaitingTimes.keepMaxTopNScores = "int | 1"
# allocationManager.repositionLowWaitingTimes.minScoreThresholdForRepositioning = "double | 0.1"
# allocationManager.repositionLowWaitingTimes.distanceWeight = "double | 0.01"
# allocationManager.repositionLowWaitingTimes.waitingTimeWeight = "double | 4.0"
# allocationManager.repositionLowWaitingTimes.demandWeight = "double | 4.0"
# allocationManager.repositionLowWaitingTimes.produceDebugImages = true
},
{
name = "Lyft",
supportedModes="ride_hail, ride_hail_pooled"
initialization.initType = "PROCEDURAL" # Other possible values - FILE
initialization.filePath = ${beam.inputDirectory}"/rideHailFleet.csv.gz"
# initialization.parking.filePath = ""
initialization.procedural.vehicleTypeId = "Car"
initialization.procedural.vehicleTypePrefix = "RH"
initialization.procedural.fractionOfInitialVehicleFleet = 0.00184043
# HOME, RANDOM_ACTIVITY, UNIFORM_RANDOM, ALL_AT_CENTER, ALL_IN_CORNER
initialization.procedural.initialLocation.name = "RANDOM_ACTIVITY"
initialization.procedural.initialLocation.home.radiusInMeters = 500
initialization.procedural.vehicleAdjustmentMethod = "INCOME_BASED"
# stopFilePath = ""
maximumWalkDistanceToStopInM = 800.0
radiusInMeters = 5000
defaultBaseCost = 5.1
defaultCostPerMile = 1.12
defaultCostPerMinute = 0.32
pooledBaseCost = 4.47
pooledCostPerMile = 0.84
pooledCostPerMinute = 0.07
allocationManager.name = "POOLING_ALONSO_MORA"
allocationManager.maxWaitingTimeInSec = 1200
allocationManager.maxExcessRideTime = 0.5 # up to +50%
allocationManager.requestBufferTimeoutInSeconds = 300
# ASYNC_GREEDY_VEHICLE_CENTRIC_MATCHING, ALONSO_MORA_MATCHING_WITH_ASYNC_GREEDY_ASSIGNMENT, ALONSO_MORA_MATCHING_WITH_MIP_ASSIGNMENT
allocationManager.matchingAlgorithm = "ALONSO_MORA_MATCHING_WITH_ASYNC_GREEDY_ASSIGNMENT"
# ALONSO MORA
allocationManager.alonsoMora.maxRequestsPerVehicle = 5
# Reposition
# allocationManager.pooledRideHailIntervalAsMultipleOfSoloRideHail = "int | 1"
# DEFAULT_REPOSITIONING_MANAGER, DEMAND_FOLLOWING_REPOSITIONING_MANAGER, INVERSE_SQUARE_DISTANCE_REPOSITIONING_FACTOR, REPOSITIONING_LOW_WAITING_TIMES, THE_SAME_LOCATION_REPOSITIONING_MANAGER, ALWAYS_BE_REPOSITIONING_MANAGER
repositioningManager.name = "INVERSE_SQUARE_DISTANCE_REPOSITIONING_FACTOR"
repositioningManager.timeout = 300
# Larger value increase probability of the ride-hail vehicle to reposition
# repositioningManager.demandFollowingRepositioningManager.sensitivityOfRepositioningToDemand = "double | 1"
# repositioningManager.demandFollowingRepositioningManager.sensitivityOfRepositioningToDemandForCAVs = "double | 1"
# repositioningManager.demandFollowingRepositioningManager.numberOfClustersForDemand = "int | 30"
# repositioningManager.demandFollowingRepositioningManager.fractionOfClosestClustersToConsider = "double | 0.2"
# repositioningManager.demandFollowingRepositioningManager.horizon = "int | 1200"
# inverse Square Distance Repositioning Factor
repositioningManager.inverseSquareDistanceRepositioningFactor.sensitivityOfRepositioningToDemand = 0.2
repositioningManager.inverseSquareDistanceRepositioningFactor.sensitivityOfRepositioningToDistance = 0.88668
repositioningManager.inverseSquareDistanceRepositioningFactor.predictionHorizon = 3600
# reposition Low Waiting Times
# allocationManager.repositionLowWaitingTimes.repositionCircleRadiusInMeters = "double | 3000"
# allocationManager.repositionLowWaitingTimes.minimumNumberOfIdlingVehiclesThresholdForRepositioning = "int | 1"
# allocationManager.repositionLowWaitingTimes.percentageOfVehiclesToReposition = "double | 0.01"
# allocationManager.repositionLowWaitingTimes.timeWindowSizeInSecForDecidingAboutRepositioning = "double | 1200"
# allocationManager.repositionLowWaitingTimes.allowIncreasingRadiusIfDemandInRadiusLow = true
# allocationManager.repositionLowWaitingTimes.minDemandPercentageInRadius = "double | 0.1"
# allocationManager.repositionLowWaitingTimes.repositioningMethod = "TOP_SCORES"
# allocationManager.repositionLowWaitingTimes.keepMaxTopNScores = "int | 1"
# allocationManager.repositionLowWaitingTimes.minScoreThresholdForRepositioning = "double | 0.1"
# allocationManager.repositionLowWaitingTimes.distanceWeight = "double | 0.01"
# allocationManager.repositionLowWaitingTimes.waitingTimeWeight = "double | 4.0"
# allocationManager.repositionLowWaitingTimes.demandWeight = "double | 4.0"
# allocationManager.repositionLowWaitingTimes.produceDebugImages = true
}]
# beam.agentsim.agents.rideHail.cav.valueOfTime = 1.00
# when range below refuelRequiredThresholdInMeters, EV Ride Hail CAVs will charge
# when range above noRefuelThresholdInMeters, EV Ride Hail CAVs will not charge
# (between these params probability of charging is linear interpolation from 0% to 100%)
# beam.agentsim.agents.rideHail.human.refuelRequiredThresholdInMeters = 32180.0 # 20 miles
# beam.agentsim.agents.rideHail.human.noRefuelThresholdInMeters = 128720.0 # 80 miles
# beam.agentsim.agents.rideHail.cav.refuelRequiredThresholdInMeters = 16090.0 # 10 miles
# beam.agentsim.agents.rideHail.cav.noRefuelThresholdInMeters = 96540.0 # 60 miles
# beam.agentsim.agents.rideHail.rangeBufferForDispatchInMeters = 10000 # do not dispatch vehicles below this range to ensure enough available to get to charger
# beam.agentsim.agents.rideHail.charging.multinomialLogit.params.drivingTimeMultiplier = "double | -0.01666667" // one minute of driving is one util
# beam.agentsim.agents.rideHail.charging.multinomialLogit.params.queueingTimeMultiplier = "double | -0.01666667" // one minute of queueing is one util
# beam.agentsim.agents.rideHail.charging.multinomialLogit.params.chargingTimeMultiplier = "double | -0.01666667" // one minute of charging is one util
# beam.agentsim.agents.rideHail.charging.multinomialLogit.params.insufficientRangeMultiplier = "double | -60.0" // indicator variable so straight 60 minute penalty if out of range
beam.agentsim.agents.freight {
enabled = true
plansFilePath = ${beam.inputDirectory}"/freight/23Jan2024/Base/freight-merged-payload-plans.csv"
toursFilePath = ${beam.inputDirectory}"/freight/23Jan2024/Base/freight-merged-tours.csv"
carriersFilePath = ${beam.inputDirectory}"/freight/23Jan2024/Base/freight-merged-carriers.csv"
reader = "Generic"
isWgs = true
generateFixedActivitiesDurations = true
name = "Freight"
tourSampleSizeAsFractionOfTotal = 1.0 # value should be within [0,1] (FRISM is providing 10% of total population of tours so not ${beam.agentsim.agentSampleSizeAsFractionOfPopulation})
carrierParkingFilePath = ""
vehicleTypesFilePath = ${beam.inputDirectory}"/vehicle-tech/freight-only-vehicletypes--tda-baseline.csv"
replanning {
disableAfterIteration = -1
#8 AM, required for wholeFleet strategy. The vehicle departure times are distributed in time interval ±1 hour
departureTime = 28800
#Possible options: singleTour (when each freight tour is rearranged) | wholeFleet
strategy = "singleTour"
}
}
# beam.agentsim.agents.activities.activityTypeToFixedDurationMap = [string]
beam.agentsim.agents.plans.inputPlansFilePath = ""
beam.agentsim.agents.plans.inputPersonAttributesFilePath = ""
# fraction of input plans (taken from the beam.input.lastBaseOutputDir) to be merged into the latest output plans
beam.agentsim.agents.plans.merge.fraction = 0.25
beam.agentsim.agents.households.inputFilePath = ""
beam.agentsim.agents.households.inputHouseholdAttributesFilePath = ""
#BeamVehicles Params
# beam.agentsim.agents.vehicles.fractionOfInitialVehicleFleet = "double | 1.0"
beam.agentsim.agents.vehicles.vehicleAdjustmentMethod = "INCOME_BASED"
# beam.agentsim.agents.vehicles.downsamplingMethod = "SECONDARY_VEHICLES_FIRST"
# beam.agentsim.agents.vehicles.fractionOfPeopleWithBicycle = "double | 1.0"
beam.agentsim.agents.vehicles.linkToGradePercentFilePath = ${beam.inputDirectory}"/r5/r5-unclassified-simplified/linkToGradePercent.csv"
beam.agentsim.agents.vehicles.fuelTypesFilePath = ${beam.inputDirectory}"/samples/beamFuelTypes.csv"
beam.agentsim.agents.vehicles.vehicleTypesFilePath = ${beam.inputDirectory}"/vehicle-tech/vehicleTypes.csv"
beam.agentsim.agents.vehicles.vehiclesFilePath = ${beam.inputDirectory}"/samples/vehicles.csv.gz"
beam.agentsim.agents.vehicles.meanPrivateVehicleStartingSOC = 0.6
beam.agentsim.agents.vehicles.linkSocAcrossIterations = true
beam.agentsim.agents.vehicles.meanRidehailVehicleStartingSOC = 0.6
beam.agentsim.agents.vehicles.roadRestrictionWeightMultiplier = 2.0
# beam.agentsim.agents.vehicles.transitVehicleTypesByRouteFile = ""
beam.agentsim.agents.vehicles.generateEmergencyHouseholdVehicleWhenPlansRequireIt = true
# beam.agentsim.agents.vehicles.replanOnTheFlyWhenHouseholdVehiclesAreNotAvailable = "boolean | false"
beam.agentsim.agents.vehicles.enroute.refuelRequiredThresholdOffsetInMeters = 32186.9 # 20 miles
beam.agentsim.agents.vehicles.enroute.noRefuelThresholdOffsetInMeters = 128748 # 80 miles
# beam.agentsim.agents.vehicles.enroute.noRefuelAtRemainingDistanceThresholdInMeters = 500 # 500 meters
# beam.agentsim.agents.vehicles.enroute.remainingDistanceWrtBatteryCapacityThreshold = 2 # this represents +/- the number of times an agent will enroute when ranger is x times lower than the remaining distance
# beam.agentsim.agents.vehicles.destination.refuelRequiredThresholdInMeters = 482803 # 300 miles
# beam.agentsim.agents.vehicles.destination.noRefuelThresholdInMeters = 482803 # 300 miles
# beam.agentsim.agents.vehicles.destination.home.refuelRequiredThresholdInMeters = 482803 # 300 miles
# beam.agentsim.agents.vehicles.destination.home.noRefuelThresholdInMeters = 482803 # 300 miles
# beam.agentsim.agents.vehicles.destination.work.refuelRequiredThresholdInMeters = 482803 # 300 miles
# beam.agentsim.agents.vehicles.destination.work.noRefuelThresholdInMeters = 482803 # 300 miles
# beam.agentsim.agents.vehicles.destination.secondary.refuelRequiredThresholdInMeters = 482803 # 300 miles
# beam.agentsim.agents.vehicles.destination.secondary.noRefuelThresholdInMeters = 482803 # 300 miles
# these must be one of the shared vehicle types
beam.agentsim.agents.vehicles.dummySharedCar.vehicleTypeId = "cv-L1-61000-2019"
# beam.agentsim.agents.vehicles.dummySharedBike.vehicleTypeId = "sharedVehicle-sharedBike"
beam.agentsim.agents.vehicles.sharedFleets = [
# {
# name = "my-fixed-non-reserving-fleet"
# managerType = "fixed-non-reserving"
# parkingFilePath = ""
#
# fixed-non-reserving {
# vehicleTypeId = "String | sharedVehicle-sharedCar",
# maxWalkingDistance = "int | 500"
# }
#
# inexhaustible-reserving {
# vehicleTypeId = "String | sharedVehicle-sharedCar"
# }
#
# fixed-non-reserving-fleet-by-taz {
# vehicleTypeId = "String | sharedVehicle-sharedCar",
# vehiclesSharePerTAZFromCSV = "String? |",
# maxWalkingDistance = "int | 500",
# fleetSize = "int | 10"
# }
#
# reposition {
# name = "my-reposition-algorithm"
# repositionTimeBin = "int | 3600",
# statTimeBin = "int | 300",
#
# min-availability-undersupply-algorithm {
# matchLimit = "int | 99999"
# }
# }
# }
]
beam.agentsim.chargingNetworkManager {
timeStepInSeconds = 300
# Overnight charging is still a work in progress and might produce unexpected results
# overnightChargingEnabled = "boolean | false"
chargingPointCountScalingFactor = ${beam.agentsim.agentSampleSizeAsFractionOfPopulation}
chargingPointCostScalingFactor = ${beam.agentsim.agentSampleSizeAsFractionOfPopulation}
chargingPointFilePath = ""
scaleUp {
enabled = false
expansionFactor = 1.0
activitiesLocationFilePath = ""
}
sitePowerManagerController {
connect = false
expectFeedback = false
numberOfFederates = 1
brokerAddress = "tcp://127.0.0.1"
coreType = "zmq"
timeDeltaProperty = 1.0
intLogLevel = 1
beamFederatePrefix = "BEAM_FED"
beamFederatePublication = "CHARGING_VEHICLES"
spmFederatePrefix = "SPM_FED"
spmFederateSubscription = "CHARGING_COMMANDS"
bufferSize = 10000000
}
powerManagerController {
connect = false
feedbackEnabled = false
brokerAddress = "tcp://127.0.0.1"
coreType = "zmq"
timeDeltaProperty = 1.0
intLogLevel = 1
beamFederateName = "BEAM_FED"
beamFederatePublication = "LOAD_DEMAND"
pmcFederateName = "GRID_FED"
pmcFederateSubscription = "POWER_LIMITS"
bufferSize = 10000000
}
}
beam.agentsim.agents.population.useVehicleSampling = true
# beam.agentsim.agents.population.industryRemovalProbabilty.enabled = "boolean | false"
# beam.agentsim.agents.population.industryRemovalProbabilty.inputFilePath = ""
# options: RemovePersonFromScenario, KeepPersonButRemoveAllActivities
# beam.agentsim.agents.population.industryRemovalProbabilty.removalStrategy = "String | RemovePersonFromScenario"
beam.exchange {
scenario {# source for now can be "Beam" or "UrbanSim"
source = "urbansim_v2"
# Input file format for scenario loader can be "xml", "csv" or "parquet"
fileFormat = "csv"
folder = ${beam.inputDirectory}"/urbansim"
# Allows to match urbansim modes to beam modes
modeMap = [
"BIKE -> bike",
"DRIVEALONEFREE -> car",
"DRIVEALONEPAY -> car",
"DRIVE_COM -> drive_transit",
"DRIVE_EXP -> drive_transit",
"DRIVE_HVY -> drive_transit",
"DRIVE_LOC -> drive_transit",
"DRIVE_LRF -> drive_transit",
"SHARED2FREE -> hov2",
"SHARED2PAY -> hov2",
"SHARED3FREE -> hov3",
"SHARED3PAY -> hov3",
"TAXI -> ride_hail",
"TNC_SHARED -> ride_hail_pooled",
"TNC_SINGLE -> ride_hail",
"WALK -> walk",
"WALK_COM -> walk_transit",
"WALK_EXP -> walk_transit",
"WALK_HVY -> walk_transit",
"WALK_LOC -> walk_transit",
"WALK_LRF -> walk_transit",
]
convertWgs2Utm = true
urbansim.activitySimEnabled = false
urbansim.scenarioLoadingTimeoutSeconds = 3000
}
output {
activity-sim-skimmer {
primary.enabled = false
secondary.enabled = false
# To filter the modes in the mapped skim using beam mode schema
secondary.beamModeFilter = [
"car",
"bike",
"drive_transit",
"hov2",
"hov3",
"ride_hail",
"ride_hail_pooled",
"walk",
"walk_transit",
]
# geo level different than beam.agentsim.taz format
secondary.taz.filePath = ""
secondary.taz.tazIdFieldName = ""
secondary.taz.tazMapping {
# To map this taz level with beam.agentsim.taz using two columns CSV file
# One of the column name should be the value of secondary.taz.tazIdFieldName
# The other column name should be the value of beam.agentsim.taz.tazIdFieldName
# If left empty, BEAM will automatically map beam.agentsim.taz to this zone system
filePath = ""
tazIdFieldNameKey = ""
tazIdFieldNameValue = ""
}
}
}
}
# Scaling and Tuning Params
# beam.agentsim.tuning.fuelCapacityInJoules = "double | 86400000"
beam.agentsim.tuning.transitCapacity = 0.5
# beam.agentsim.tuning.transitPrice = "double | 1.0"
# beam.agentsim.tuning.tollPrice = "double | 1.0"
# beam.agentsim.tuning.rideHailPrice = "double | 1.0"
# Population Adjustmnet
# beam.agentsim.populationAdjustment = "DEFAULT_ADJUSTMENT"
# Transit Scenarios
# beam.agentsim.scenarios.frequencyAdjustmentFile = ${beam.inputDirectory}"/r5/r5-unclassified-simplified/FrequencyAdjustment.csv"
# PhysSim Scaling Params
beam.physsim.flowCapacityFactor = 0.135
beam.physsim.storageCapacityFactor = 0.5
# beam.physsim.speedScalingFactor = "double | 1.0"
# beam.physsim.maxLinkLengthToApplySpeedScalingFactor = "double | 50.0"
beam.physsim.linkStatsWriteInterval = ${beam.outputs.defaultWriteInterval}
# beam.physsim.linkStatsBinSize = "int | 3600"
beam.physsim.ptSampleSize = ${beam.agentsim.agentSampleSizeAsFractionOfPopulation}
beam.physsim.eventsForFullVersionOfVia = true
# beam.physsim.eventsSampling = "double | 1.0"
# beam.physsim.initializeRouterWithFreeFlowTimes = true
# heads up beam.physsim.minCarSpeedInMetersPerSecond affects beam.physsim.jdeqsim.cacc
beam.physsim.minCarSpeedInMetersPerSecond = 1.0
beam.physsim.inputNetworkFilePath = ${beam.inputDirectory}"/r5/r5-unclassified-simplified/physsim-network.xml"
beam.physsim.events.fileOutputFormats = "csv.gz" # valid options: xml(.gz) , csv(.gz), none - DEFAULT: csv.gz
beam.physsim.events.eventsToWrite = "PersonArrivalEvent,PersonDepartureEvent,ActivityEndEvent,ActivityStartEvent,PersonEntersVehicleEvent,PersonLeavesVehicleEvent,ModeChoiceEvent,PathTraversalEvent,ReserveRideHailEvent,ReplanningEvent,RefuelSessionEvent,ChargingPlugInEvent,ChargingPlugOutEvent,ParkingEvent,LeavingParkingEvent,PersonCostEvent,TeleportationEvent"
# The `duplicatePTE` group of configs allows to increase physSim population without increasing agentSim population.
# The idea behind that is the following - bigger physSim population allows to use higher values of flowCapacityFactor
# thus reducing the rounding error for links capacity.
# This should allow better speed calibration without too high agentSim population.
# beam.physsim.duplicatePTE.fractionOfEventsToDuplicate = "double | 0.0"
# beam.physsim.duplicatePTE.departureTimeShiftMin = "int | -600"
# beam.physsim.duplicatePTE.departureTimeShiftMax = "int | 600"
beam.physsim.skipPhysSim = false
# values: JDEQSim, BPRSim, PARBPRSim, CCHRoutingAssignment
beam.physsim.name = "PARBPRSim"
# values: Auto, Sequential, Parallel
# beam.physsim.eventManager.type = "Auto"
# number of threads for parallel event manager,
# 1 thread usually shows the best performance (async event handling)
# beam.physsim.eventManager.numberOfThreads = "int | 1"
# beam.physsim.pickUpDropOffAnalysis.enabled = "boolean | false"
# beam.physsim.pickUpDropOffAnalysis.secondsFromPickUpPropOffToAffectTravelTime = "int | 600"
# beam.physsim.pickUpDropOffAnalysis.additionalTravelTimeMultiplier = "double | 1.0"
# JDEQSim
# beam.physsim.jdeqsim.agentSimPhysSimInterfaceDebugger.enabled = false
beam.physsim.jdeqsim.cacc.enabled = true
# beam.physsim.jdeqsim.cacc.minRoadCapacity = "int | 2000"
# beam.physsim.jdeqsim.cacc.minSpeedMetersPerSec = "int | 20"
# beam.physsim.jdeqsim.cacc.speedAdjustmentFactor = "double | 1.0"
# beam.physsim.jdeqsim.cacc.capacityPlansWriteInterval = 0
# The following parameter has been replaced by beam.physsim.minCarSpeedInMetersPerSecond = 2.0
# beam.physsim.jdeqsim.cacc.adjustedMinimumRoadSpeedInMetersPerSecond = "double | 1.3"
# values: BPR, FREE_FLOW
# beam.physsim.bprsim.travelTimeFunction = "BPR"
# beam.physsim.bprsim.minFlowToUseBPRFunction = "int | 0"
# beam.physsim.bprsim.inFlowAggregationTimeWindowInSeconds = "int | 900"
# beam.physsim.parbprsim.numberOfClusters = "int | 8"
# sync of bpr sim clusters every syncInterval seconds
# beam.physsim.parbprsim.syncInterval = "int | 60"
# CCHRoutingAssignment
#Used to calculate ods number multiplier with following formula:
#1 / agentSampleSizeAsFractionOfPopulation * congestionFactor
# beam.physsim.cchRoutingAssignment.congestionFactor = "double | 1.0"
beam.physsim.overwriteLinkParamPath = ""
# Possible type is one of: normal, experiment_2.0, experiment_2.1, experiment_3.0, experiment_4.0, experiment_5.0, experiment_5.1, experiment_5.2, consecutive_increase_of_population
# beam.physsim.relaxation.type = "normal"
# beam.physsim.relaxation.experiment2_0.internalNumberOfIterations = "int | 15"
# beam.physsim.relaxation.experiment2_0.fractionOfPopulationToReroute = "double | 0.1"
# beam.physsim.relaxation.experiment2_0.clearRoutesEveryIteration = "boolean | true"
# beam.physsim.relaxation.experiment2_0.clearModesEveryIteration = "boolean | true"
# beam.physsim.relaxation.experiment2_1.internalNumberOfIterations = "int | 15"
# beam.physsim.relaxation.experiment2_1.fractionOfPopulationToReroute = "double | 0.1"
# beam.physsim.relaxation.experiment2_1.clearRoutesEveryIteration = "boolean | true"
# beam.physsim.relaxation.experiment2_1.clearModesEveryIteration = "boolean | true"
# beam.physsim.relaxation.experiment3_0.internalNumberOfIterations = "int | 15"
# beam.physsim.relaxation.experiment3_0.fractionOfPopulationToReroute = "double | 0.1"
# beam.physsim.relaxation.experiment4_0.percentToSimulate = [double]
# beam.physsim.relaxation.experiment5_0.percentToSimulate = [double]
# beam.physsim.relaxation.experiment5_1.percentToSimulate = [double]
# beam.physsim.relaxation.experiment5_2.percentToSimulate = [double]
beam.physsim.network.overwriteRoadTypeProperties {
enabled = true
motorway { #Freeway
speed = 31.2928
# capacity = "int?"
# lanes = "int?"
alpha = 0.83
beta = 5.5
}
motorwayLink { #Freeway-to-Freeway Connector
speed = 26.83
capacity = 4000
# lanes = "int?"
alpha = 0.83
beta = 5.5
}
primary { #Super Arterial
speed = 14.88
# capacity = "int?"
# lanes = "int?"
alpha = 0.6
beta = 3.5
}
primaryLink { #Ramp
speed = 13.3816
capacity = 4000
# lanes = "int?"
alpha = 0.83
beta = 5.5
}
trunk { #Expressway
speed = 25.8224
# capacity = "int?"
# lanes = "int?"
alpha = 0.71
beta = 2.1
}
trunkLink { #Ramp
speed = 20.352
capacity = 3000
# lanes = "int?"
alpha = 0.83
beta = 5.5
}
secondary { #Major Arterial
speed = 10.5
# capacity = "int?"
# lanes = "int?"
alpha = 0.6
beta = 3.5
}
secondaryLink { #Ramp
speed = 9.5
capacity = 3000
# lanes = "int?"
alpha = 0.83
beta = 5.5
}
tertiary { #Minor Arterial
speed = 9.5
# capacity = "int?"
# lanes = "int?"
alpha = 0.6
beta = 3.5
}
tertiaryLink { #Ramp
speed = 8.94
# capacity = "int?"
# lanes = "int?"
alpha = 0.83
beta = 5.5
}
minor { #Collector
# speed = "double?"
# capacity = "int?"
# lanes = "int?"
alpha = 0.6
beta = 8.5
}
residential { #Local Street
speed = 8.94
# capacity = "int?"
# lanes = "int?"
alpha = 0.6
beta = 8.5
}
livingStreet { #Alley
speed = 8.94
# capacity = "int?"
# lanes = "int?"
alpha = 0.6
beta = 8.5
}
unclassified { #Collector
speed = 14.0
capacity = 1200
# lanes = "int?"
alpha = 0.6
beta = 8.5
}
default {
alpha = 1.0
beta = 2.0
}
}
beam.physsim.network.maxSpeedInference {
enabled = false
# Possible types of inference: MEAN, MEDIAN
type = "MEAN"
}
# For a small test OSM map (10-20 nodes) it might be possible that R5 TransportNetwork would incorrectly consider
# all nodes to be an island and will remove it. Set this to `false` to override that.
# beam.physsim.network.removeIslands = "boolean | true"
# Replanning
beam.replanning.replanningPenaltyInDollars = 100.0
beam.replanning.maxAgentPlanMemorySize = 3
beam.replanning.planSelectionBeta = 1.0
beam.replanning.Module_1 = "SelectExpBeta"
beam.replanning.ModuleProbability_1 = 0.7
beam.replanning.Module_2 = "ClearRoutes"
beam.replanning.ModuleProbability_2 = 0.1
beam.replanning.Module_3 = "ClearModes"
beam.replanning.ModuleProbability_3 = 0.1
beam.replanning.Module_4 = "TimeMutator"
beam.replanning.ModuleProbability_4 = 0.1
# beam.replanning.fractionOfIterationsToDisableInnovation = "double | Double.PositiveInfinity"
beam.replanning.clearModes.modes = []
beam.replanning.clearModes.iteration = 0
#clear mode strategy options: AtBeginningOfIteration | AtBeginningAndAllSubsequentIterations
beam.replanning.clearModes.strategy = "AtBeginningOfIteration"
#skims
beam.router.skim = {
keepKLatestSkims = 1
writeSkimsInterval = ${beam.outputs.defaultWriteInterval}
writeAggregatedSkimsInterval = ${beam.outputs.defaultWriteInterval}
activity-sim-skimmer {
name = "activity-sim-skimmer"
fileBaseName = "activitySimODSkims"
#values: csv | omx
fileOutputFormat = "omx"
}
drive-time-skimmer {
name = "drive-time-skimmer"
fileBaseName = "skimsTravelTimeObservedVsSimulated"
}
origin-destination-skimmer {
name = "od-skimmer"
fileBaseName = "skimsOD"
writeAllModeSkimsForPeakNonPeakPeriodsInterval = 0
writeFullSkimsInterval = 0
poolingTravelTimeOverheadFactor = 1.21
}
taz-skimmer {
name = "taz-skimmer"
fileBaseName = "skimsTAZ"
geoHierarchy = "TAZ" # TAZ or H3
}
transit-crowding-skimmer {
name = "transit-crowding-skimmer"
fileBaseName = "skimsTransitCrowding"
}
}
#h3taz
#beam.agentsim.h3taz = {
# lowerBoundResolution = "int | 6"
# upperBoundResolution = "int | 9"
#}
##################################################################
# Warm Mode
##################################################################
# valid options:
# * disabled
# * full
# * linkStatsOnly (only link stats is loaded (all the other data is got from the input directory))
# * linkStatsFromLastRun (only link stats is loaded from beam.input.lastBaseOutputDir directory)
beam.warmStart.type = "linkStatsFromLastRun"
# beam.warmStart.samplePopulationIntegerFlag = 0 # Int chosen instead of Boolean, as passthrough coverts Boolean to Int
#PATH can be a directory or zip archive of the output directory (e.g. like what get's stored on S3), including a URL to an S3 output.
beam.warmStart.path = ""
# If the full warmstart directory isn't being used, it is possible to only load in a linkStats file instead to pre-
# populate the link travel times. Add path to linkStats file here
beam.warmStart.initialLinkstatsFilePath = ${beam.inputDirectory}"/r5/r5-unclassified-simplified/0.linkstats.seattle-baseline-20240912.csv.gz"
#creates warmstart_data.zip that can be used for warmstart
# beam.warmStart.prepareData = false
# beam.warmStart.skimsFilePaths = [ # Filled by warmstart
# {
# skimType = "String"
# skimsFilePath = ""
# }
# ]
##################################################################
# Debugging
##################################################################
beam.debug {
debugEnabled = false
agentTripScoresInterval = 0
triggerMeasurer {
enabled = false
writeStuckAgentDetectionConfig = true
}
stuckAgentDetection {
enabled = false
checkIntervalMs = 200
defaultTimeoutMs = 60000
overallSimulationTimeoutMs = 100000
checkMaxNumberOfMessagesEnabled = true
thresholds = [
# Possible values (with deviations from default):
# triggerType = "beam.agentsim.agents.PersonAgent$ActivityStartTrigger" && markAsStuckAfterMs = 20s
# triggerType = "beam.agentsim.agents.PersonAgent$ActivityEndTrigger" && markAsStuckAfterMs = 60s
# triggerType = "beam.agentsim.agents.PersonAgent$PersonDepartureTrigger"
# triggerType = "beam.agentsim.agents.modalbehaviors.DrivesVehicle$StartLegTrigger" && markAsStuckAfterMs = 18s
# triggerType = "beam.agentsim.agents.modalbehaviors.DrivesVehicle$EndLegTrigger" && markAsStuckAfterMs = 60s
# triggerType = "beam.agentsim.agents.modalbehaviors.DrivesVehicle$AlightVehicleTrigger" && markAsStuckAfterMs = 21s
# triggerType = "beam.agentsim.agents.modalbehaviors.DrivesVehicle$BoardVehicleTrigger" && markAsStuckAfterMs = 21s
# triggerType = "beam.agentsim.agents.modalbehaviors.DrivesVehicle$StartRefuelSessionTrigger" && markAsStuckAfterMs = 21s
# riggerType = "beam.agentsim.agents.modalbehaviors.DrivesVehicle$EndRefuelSessionTrigger" && markAsStuckAfterMs = 21s
# triggerType = "beam.agentsim.agents.InitializeTrigger"
# triggerType = "beam.agentsim.agents.ridehail.RideHailManager$BufferedRideHailRequestsTimeout"
# triggerType = "beam.agentsim.agents.ridehail.RideHailManager$RideHailAllocationManagerTimeout" && markAsStuckAfterMs = 40s
{
actorTypeToMaxNumberOfMessages {
population = 1
rideHailAgent = 1
transitDriverAgent = 1
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.agents.InitializeTrigger"
},
{
actorTypeToMaxNumberOfMessages {
population = 11
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.agents.PersonAgent$ActivityEndTrigger"
},
{
actorTypeToMaxNumberOfMessages {
population = 1
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.agents.PersonAgent$ActivityStartTrigger"
},
{
actorTypeToMaxNumberOfMessages {
population = 20
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.agents.PersonAgent$PersonDepartureTrigger"
},
{
actorTypeToMaxNumberOfMessages {
population = 20
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.agents.modalbehaviors.DrivesVehicle$AlightVehicleTrigger"
},
{
actorTypeToMaxNumberOfMessages {
population = 20
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.agents.modalbehaviors.DrivesVehicle$BoardVehicleTrigger"
},
{
actorTypeToMaxNumberOfMessages {
population = 80,
rideHailAgent = 104,
transitDriverAgent = 114
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.agents.modalbehaviors.DrivesVehicle$EndLegTrigger"
},
{
actorTypeToMaxNumberOfMessages {
population = 80,
rideHailAgent = 104,
transitDriverAgent = 114
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.agents.modalbehaviors.DrivesVehicle$StartLegTrigger"
},
{
actorTypeToMaxNumberOfMessages {
transitDriverAgent = 1
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.scheduler.BeamAgentScheduler$KillTrigger"
}
]
}
debugActorTimerIntervalInSec = 0
actor.logDepth = 2
memoryConsumptionDisplayTimeoutInSec = 0
clearRoutedOutstandingWorkEnabled = false
secondsToWaitToClearRoutedOutstandingWork = 60
vmInformation.createGCClassHistogram = false
# it implies vmInformation.createGCClassHistogram = true
vmInformation.writeHeapDump = false
writeModeChoiceAlternatives = false
writeRealizedModeChoiceFile = false
messageLogging = false
# the max of the next 2 values is taken for the initialization step
maxSimulationStepTimeBeforeConsideredStuckMin = 60
maxSimulationStepTimeBeforeConsideredStuckAtInitializationMin = 600
}
# beam.logger.keepConsoleAppenderOn = "boolean | true"
##################################################################
# Metrics
##################################################################
# beam.metrics.level = "verbose"