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urbansim-50k_Cal2.conf
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include "../common/akka.conf"
include "../common/akka-router.conf"
include "../common/metrics.conf"
include "../common/matsim.conf"
# This version, base-sf-light.conf, is configured to use a subsample of the population located in:
# ${beam.inputDirectory}"/sample"
##################################################################
# Agentsim
##################################################################
beam.agentsim.simulationName = "urbansim-50k"
beam.agentsim.agentSampleSizeAsFractionOfPopulation = 1.0
beam.agentsim.firstIteration = 0
beam.agentsim.lastIteration = 30
beam.agentsim.thresholdForWalkingInMeters = 100
beam.agentsim.timeBinSize = 3600
beam.agentsim.startTime = "00:00:00"
beam.agentsim.endTime = "30:00:00"
beam.agentsim.schedulerParallelismWindow = 30
# MODE CHOICE OPTIONS:
# ModeChoiceMultinomialLogit ModeChoiceTransitIfAvailable ModeChoiceDriveIfAvailable ModeChoiceRideHailIfAvailable
# ModeChoiceUniformRandom
beam.agentsim.agents.modalBehaviors.modeChoiceClass = "ModeChoiceMultinomialLogit"
beam.agentsim.agents.modalBehaviors.defaultValueOfTime = 23.1
beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.transfer = -4.0
beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.car_intercept = -3.25
beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.walk_transit_intercept = 2.5
beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.drive_transit_intercept = -5.0
beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.ride_hail_transit_intercept =13.0
beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.ride_hail_intercept = -2.75
beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.ride_hail_pooled_intercept = -16.5
beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.walk_intercept =14.5
beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.bike_intercept = 6.75
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.gender = 0
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.age_30_to_50 = 0.319
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.age_50_to_70 = 0.783
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.age_70_over = 0.443
#beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.income_under_35k = -0.652
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.income_under_35k = -0.3
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.income_35_to_100k = 0.0
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.income_100k_more = -0.108
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.workTrip = -0.263
#beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.linkToTransit = -.759
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHailPooled.car_owner = -.572
beam.agentsim.agents.modalBehaviors.otherMultiplier.transit.income_under_35k = -0.25
beam.agentsim.agents.modalBehaviors.otherMultiplier.walk.income_under_35k = 0.3
beam.agentsim.agents.modalBehaviors.otherMultiplier.bike.income_under_35k = 0.1
beam.agentsim.agents.modalBehaviors.otherMultiplier.drive.income_under_35k = -0.2
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHail.income_under_35k = 0.1
beam.agentsim.agents.modalBehaviors.otherMultiplier.rideHail.income_100k_more = -0.1
## conversion for low- to high-income in-veh VOT
beam.agentsim.agents.modalBehaviors.incomeMultiplier.inVehTime = 2
beam.agentsim.agents.modalBehaviors.modeVotMultiplier.bike = 3.5
beam.agentsim.agents.modalBehaviors.modeVotMultiplier.waiting = 2
beam.agentsim.agents.modalBehaviors.modeVotMultiplier.walk = 1.5
beam.agentsim.agents.modalBehaviors.modeVotMultiplier.transit = 1.0
beam.agentsim.agents.modalBehaviors.lccm.filePath = ${beam.inputDirectory}"/lccm-long.csv"
#beam.agentsim.agents.modeIncentive.filePath = ${beam.inputDirectory}"/incentivesall_transit1_25LowIncome.csv"
beam.agentsim.agents.ptFare.filePath = ${beam.inputDirectory}"/ptFaresAltered.csv"
beam.agentsim.agents.plans {
inputPlansFilePath = ${beam.inputDirectory}"/sample/50k/population2.xml"
inputPersonAttributesFilePath = ${beam.inputDirectory}"/sample/50k/populationAttributes2.xml"
}
beam.agentsim.agents.households {
inputFilePath = ${beam.inputDirectory}"/sample/50k/households2.xml"
inputHouseholdAttributesFilePath = ${beam.inputDirectory}"/sample/50k/householdAttributes2.xml"
}
beam.exchange.scenario {
# source for now can be "MATSim" or "UrbanSim"
source = "UrbanSim"
# Input file format for scenario loader can be "csv" or "parquet"
fileFormat = "csv"
folder = ${beam.inputDirectory}"/urbansim/50k/"
convertWgs2Utm = true
}
#BeamVehicles Params
beam.agentsim.agents.population.useVehicleSampling = true
beam.agentsim.agents.vehicles.vehicleAdjustmentMethod = "INCOME_BASED"
beam.agentsim.agents.vehicles.linkToGradePercentFilePath = ${beam.inputDirectory}"/r5/linkToGradePercent.csv"
beam.agentsim.agents.vehicles.fuelTypesFilePath = ${beam.inputDirectory}"/beamFuelTypes.csv"
beam.agentsim.agents.vehicles.vehicleTypesFilePath = ${beam.inputDirectory}"/vehicleTypesAltered2.csv"
beam.agentsim.agents.vehicles.vehiclesFilePath = ${beam.inputDirectory}"/sample/50k/vehicles.csv"
beam.agentsim.agents.vehicles.sharedFleets = []
#TAZ params
beam.agentsim.taz.filePath=${beam.inputDirectory}"/taz-centers.csv.gz"
beam.agentsim.taz.parkingFilePath = ${beam.inputDirectory}"/taz-parking.csv.gz"
#beam.agentsim.taz.parkingStallCountScalingFactor = 1.0
#Toll params
beam.agentsim.toll.filePath=${beam.inputDirectory}"/toll-prices.csv"
# Scaling and Tuning Params
beam.agentsim.tuning.transitCapacity = 0.5
beam.agentsim.tuning.transitPrice = 1.0
beam.agentsim.tuning.tollPrice = 1.0
beam.agentsim.tuning.rideHailPrice = 1.0
###########################
# Physsim
###########################
beam.physsim.inputNetworkFilePath = ${beam.inputDirectory}"/r5/physsim-network.xml"
beam.physsim.flowCapacityFactor = 0.01
beam.physsim.storageCapacityFactor = 1.0
beam.physsim.writeMATSimNetwork = false
beam.physsim.linkStatsBinSize = 3600
beam.physsim.ptSampleSize = 0.03
beam.physsim.jdeqsim.agentSimPhysSimInterfaceDebugger.enabled = false
beam.physsim.skipPhysSim = false
#beam.physsim.relaxation.type="experiment_5.0"
###########################
# Replanning
###########################
beam.replanning{
maxAgentPlanMemorySize = 6
Module_1 = "SelectExpBeta"
ModuleProbability_1 = 0.7
Module_2 = "ClearRoutes"
ModuleProbability_2 = 0.1
Module_3 = "ClearModes"
ModuleProbability_3 = 0.1
Module_4 = "TimeMutator"
ModuleProbability_4 = 0.0
}
##################################################################
# Warm Mode
##################################################################
beam.warmStart.enabled = false
#PATH TYPE OPTIONS: PARENT_RUN, ABSOLUTE_PATH
#PARENT_RUN: can be a director or zip archive of the output directory (e.g. like what get's stored on S3). We should also be able to specify a URL to an S3 output.
#ABSOLUTE_PATH: a directory that contains required warm stats files (e.g. linkstats and eventually a plans).
beam.warmStart.path = "https://s3.us-east-2.amazonaws.com/beam-outputs/run149-base__2018-06-27_20-28-26_2a2e2bd3.zip"
##################################################################
# RideHail
##################################################################
# Initialization Type(PROCEDURAL | FILE)
beam.agentsim.agents.rideHail.initialization.initType = "PROCEDURAL"
# If PROCEDURAL, use these params
# initialization.procedural.initialLocation.name(INITIAL_RIDE_HAIL_LOCATION_HOME | INITIAL_RIDE_HAIL_LOCATION_UNIFORM_RANDOM | INITIAL_RIDE_HAIL_LOCATION_ALL_AT_CENTER | INITIAL_RIDE_HAIL_LOCATION_ALL_IN_CORNER)
beam.agentsim.agents.rideHail.initialization.procedural.initialLocation.name = "RANDOM_ACTIVITY"
beam.agentsim.agents.rideHail.initialization.procedural.initialLocation.home.radiusInMeters = 500
beam.agentsim.agents.rideHail.initialization.procedural.vehicleTypeId="Car"
#beam.agentsim.agents.rideHail.initialization.procedural.fractionOfInitialVehicleFleet = 0.01 //How many ride hail agents, as a portion of initial total personal vehicles
beam.agentsim.agents.rideHail.initialization.procedural.numDriversAsFractionOfPopulation=0.01
# If FILE, use this param
beam.agentsim.agents.rideHail.initialization.filePath=${beam.inputDirectory}"/rideHailFleet.csv"
# Ride Hailing General Params
beam.agentsim.agents.rideHail.defaultBaseCost= 2.20
beam.agentsim.agents.rideHail.defaultCostPerMile=1.33
beam.agentsim.agents.rideHail.defaultCostPerMinute=0.24
beam.agentsim.agents.rideHail.refuelThresholdInMeters=20000.0
beam.agentsim.agents.rideHail.rideHailManager.radiusInMeters=10000
beam.agentsim.agents.rideHail.iterationStats.timeBinSizeInSec=3600
# Ride Hailing Pooled Params
beam.agentsim.agents.rideHail.pooledBaseCost= 2.2
beam.agentsim.agents.rideHail.pooledCostPerMile=1.29
beam.agentsim.agents.rideHail.pooledCostPerMinute=0.17
# SurgePricing parameters
beam.agentsim.agents.rideHail.surgePricing.surgeLevelAdaptionStep=0.1
beam.agentsim.agents.rideHail.surgePricing.minimumSurgeLevel=0.1
# priceAdjustmentStrategy(KEEP_PRICE_LEVEL_FIXED_AT_ONE | CONTINUES_DEMAND_SUPPLY_MATCHING)
beam.agentsim.agents.rideHail.surgePricing.priceAdjustmentStrategy="KEEP_PRICE_LEVEL_FIXED_AT_ONE"
# allocationManager(DEFAULT_MANAGER | POOLING_ALONSO_MORA)
beam.agentsim.agents.rideHail.allocationManager.name="POOLING_ALONSO_MORA"
beam.agentsim.agents.rideHail.allocationManager.requestBufferTimeoutInSeconds = 150
# repositioningManager can be DEFAULT_REPOSITIONING_MANAGER | DEMAND_FOLLOWING_REPOSITIONING_MANAGER | REPOSITIONING_LOW_WAITING_TIMES
beam.agentsim.agents.rideHail.repositioningManager.name="DEFAULT_REPOSITIONING_MANAGER"
beam.agentsim.agents.rideHail.repositioningManager.timeout=300
# DEMAND_FOLLOWING_REPOSITIONING_MANAGER
beam.agentsim.agents.rideHail.repositioningManager.demandFollowingRepositioningManager.sensitivityOfRepositioningToDemand=1
beam.agentsim.agents.rideHail.repositioningManager.demandFollowingRepositioningManager.numberOfClustersForDemand=30
# REPOSITIONING_LOW_WAITING_TIMES
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.percentageOfVehiclesToReposition=0.01
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.timeWindowSizeInSecForDecidingAboutRepositioning=1200
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.allowIncreasingRadiusIfDemandInRadiusLow=true
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.minDemandPercentageInRadius=0.1
# repositioningMethod(TOP_SCORES | KMEANS)
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.repositioningMethod="TOP_SCORES"
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.keepMaxTopNScores=5
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.minScoreThresholdForRepositioning=0.00001
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.distanceWeight=0.01
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.waitingTimeWeight=4.0
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.demandWeight=4.0
beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.produceDebugImages=true
##################################################################
# OUTPUTS
##################################################################
# The baseOutputDirectory is the base directory where outputs will be written. The beam.agentsim.simulationName param will
# be used as the name of a sub-directory beneath the baseOutputDirectory for simulation results.
# If addTimestampToOutputDirectory == true, a timestamp will be added, e.g. "beamville_2017-12-18_16-48-57"
beam.outputs.baseOutputDirectory = ${PWD}"/output/sf-light"
beam.outputs.baseOutputDirectory = ${?BEAM_OUTPUT}
beam.outputs.addTimestampToOutputDirectory = true
# To keep all logging params in one place, BEAM overrides MATSim params normally in the controller config module
beam.physsim.linkStatsWriteInterval = 10
beam.outputs.writeSkimsInterval = 10
beam.outputs.writePlansInterval = 10
beam.outputs.writeEventsInterval = 5
# The remaining params customize how events are written to output files
beam.outputs.events.fileOutputFormats = "csv.gz" # valid options: xml(.gz) , csv(.gz), none - DEFAULT: csv.gz
# Events Writing Logging Levels:
# Any event types not explicitly listed in overrideWritingLevels take on defaultWritingLevel
beam.outputs.events.eventsToWrite = "ActivityEndEvent,ActivityStartEvent,PersonEntersVehicleEvent,PersonLeavesVehicleEvent,ModeChoiceEvent,PathTraversalEvent,ReserveRideHailEvent,ReplanningEvent,LeavingParkingEvent,PersonCostEvent"
beam.outputs.stats.binSize = 3600
##################################################################
# Debugging
##################################################################
beam.debug.debugEnabled = true
beam.debug.actor.logDepth = 0
beam.debug.debugActorTimerIntervalInSec = 60
beam.debug.stuckAgentDetection {
checkIntervalMs = 200
checkMaxNumberOfMessagesEnabled = false
defaultTimeoutMs = 60000
enabled = false
overallSimulationTimeoutMs = 100000
thresholds = [
{
actorTypeToMaxNumberOfMessages {
population = 1
rideHailAgent = 1
transitDriverAgent = 1
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.agents.InitializeTrigger"
},
{
actorTypeToMaxNumberOfMessages {
population = 11
}
markAsStuckAfterMs = 20000
triggerType = "beam.agentsim.agents.PersonAgent$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"
}
]
}
##################################################################
# SPATIAL
##################################################################
beam.spatial = {
localCRS = "epsg:26910" # what crs to use for distance calculations, must be in units of meters
boundingBoxBuffer = 10000 # meters of buffer around network for defining extend of spatial indices
}
##################################################################
# BEAM ROUTING SERVICE
##################################################################
beam.routing {
#Base local date in ISO 8061 YYYY-MM-DDTHH:MM:SS+HH:MM
transitOnStreetNetwork = true
baseDate = "2017-09-22T00:00:00-07:00"
r5 {
directory = ${beam.inputDirectory}"/r5"
# Departure window in min
departureWindow = 1.0167
osmFile = ${beam.inputDirectory}"/r5/sf-light.osm.pbf"
osmMapdbFile = ${beam.inputDirectory}"/r5/osm.mapdb"
mNetBuilder.fromCRS = "epsg:4326" # WGS84
mNetBuilder.toCRS = ${beam.spatial.localCRS}
}
}
##################################################################
# Calibration
##################################################################
beam.calibration.objectiveFunction = "CountsObjectiveFunction"
beam.calibration.mode.benchmarkFilePath=${beam.inputDirectory}"/sf-light-calibration/benchmark.csv"
beam.calibration.counts {
countsScaleFactor = 10.355
writeCountsInterval = 1
averageCountsOverIterations = 1
inputCountsFile = ${beam.inputDirectory}"/counts/fall_2015_wed_filtered.xml"
}
beam.competition.submissionScoreMSAOverNumberOfIters=1