Min-utility best response from rideHailMaster.

src/main/resources/beam-template.conf

@@ -124,6 +124,7 @@ beam.agentsim.agents.modalBehaviors.multinomialLogit.params.ride_hail_pooled_int
 beam.agentsim.agents.modalBehaviors.multinomialLogit.params.walk_intercept = "double | 0.0"
 beam.agentsim.agents.modalBehaviors.multinomialLogit.params.bike_intercept = "double | 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"
 
@@ -193,6 +194,9 @@ beam.agentsim.taz.parkingManager.parallel.numberOfClusters = "int | 8"
 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 Params
 
 beam.agentsim.agents.rideHail.managers = [{

src/main/scala/beam/agentsim/agents/ridehail/RideHailMaster.scala

@@ -3,10 +3,14 @@ package beam.agentsim.agents.ridehail
 import akka.actor.{ActorRef, Props, Terminated}
 import beam.agentsim.agents.BeamAgent.Finish
 import beam.agentsim.agents.InitializeTrigger
+import beam.agentsim.agents.choice.logit.{MultinomialLogit, UtilityFunctionOperation}
 import beam.agentsim.agents.ridehail.RideHailManager.ResponseCache
+import beam.agentsim.agents.ridehail.RideHailManager.TravelProposal
 import beam.agentsim.agents.ridehail.RideHailMaster.RequestWithResponses
 import beam.agentsim.agents.vehicles.AccessErrorCodes.UnknownInquiryIdError
-import beam.agentsim.agents.vehicles.VehicleManager
+import beam.agentsim.agents.vehicles.{PersonIdWithActorRef, VehicleManager}
+import beam.sim.population.AttributesOfIndividual
+import beam.sim.population.PopulationAdjustment._
 import beam.agentsim.scheduler.BeamAgentScheduler.{CompletionNotice, ScheduleTrigger}
 import beam.agentsim.scheduler.Trigger.TriggerWithId
 import beam.router.RouteHistory
@@ -84,6 +88,7 @@ class RideHailMaster(
   private val inquiriesWithResponses: mutable.Map[Int, RequestWithResponses] = mutable.Map.empty
   private val rideHailResponseCache = new ResponseCache
   val rand: Random = new Random(beamScenario.beamConfig.matsim.modules.global.randomSeed)
+  private val bestResponseType: String = beamServices.beamConfig.beam.agentsim.agents.rideHail.bestResponseType
 
   override def loggedReceive: Receive = {
     case TriggerWithId(trigger: InitializeTrigger, triggerId) =>
@@ -136,17 +141,79 @@ class RideHailMaster(
 
   private def findBestProposal(customer: Id[Person], responses: IndexedSeq[RideHailResponse]) = {
     val responsesInRandomOrder = rand.shuffle(responses)
-    val withProposals = responses.filter(_.travelProposal.isDefined)
+    val withProposals = responsesInRandomOrder.filter(_.travelProposal.isDefined)
     if (withProposals.isEmpty) responsesInRandomOrder.head
     else
-      withProposals.minBy { response =>
-        val travelProposal = response.travelProposal.get
+      bestResponseType match {
+        case "MIN_COST" =>
+          withProposals.minBy { response =>
+            val travelProposal = response.travelProposal.get
+            val price = travelProposal.estimatedPrice(customer)
+            if (travelProposal.poolingInfo.isDefined && response.request.asPooled)
+              Math.min(price, price * travelProposal.poolingInfo.get.costFactor)
+            else
+              price
+          }
+        case "MIN_UTILITY" => sampleProposals(customer, withProposals)
+      }
+  }
+
+  private def sampleProposals(customer: Id[Person], responses: IndexedSeq[RideHailResponse]): RideHailResponse = {
+    val proposalsToSample: Map[RideHailResponse, Map[String, Double]] =
+      proposalsToResponseAlternatives(customer, responses)
+    val mnlParams = Map(
+      "cost"         -> UtilityFunctionOperation.Multiplier(-1.0),
+      "subscription" -> UtilityFunctionOperation.Multiplier(1.0)
+    )
+    val mnl: MultinomialLogit[RideHailResponse, String] = MultinomialLogit(Map.empty, mnlParams)
+    val proposalsWithUtility = mnl.calcAlternativesWithUtility(proposalsToSample)
+    val chosenProposal = mnl.sampleAlternative(proposalsWithUtility, rand)
+    chosenProposal.get.alternativeType
+  }
+
+  private def proposalsToResponseAlternatives(
+    customer: Id[Person],
+    responses: IndexedSeq[RideHailResponse]
+  ): Map[RideHailResponse, Map[String, Double]] = {
+    val person = beamServices.matsimServices.getScenario.getPopulation.getPersons.get(customer)
+    val customerAttributes = person.getCustomAttributes.get(BEAM_ATTRIBUTES).asInstanceOf[AttributesOfIndividual]
+    responses.map { alt =>
+      val cost: Double = {
+        val travelProposal = alt.travelProposal.get
         val price = travelProposal.estimatedPrice(customer)
-        if (travelProposal.poolingInfo.isDefined && response.request.asPooled)
+        if (travelProposal.poolingInfo.isDefined && alt.request.asPooled)
           Math.min(price, price * travelProposal.poolingInfo.get.costFactor)
         else
           price
       }
+      val scaledTime: Double = customerAttributes.getVOT(
+        getGeneralizedTimeOfProposalInHours(alt.request.customer, alt.travelProposal)
+      )
+      val hasSubscription =
+        if (alt.request.rideHailServiceSubscription.contains(alt.rideHailManagerName)) 1.0 else 0.0
+
+      alt ->
+      Map(
+        "cost"         -> (cost + scaledTime),
+        "subscription" -> hasSubscription * beamServices.beamConfig.beam.agentsim.agents.modalBehaviors.multinomialLogit.params.ride_hail_subscription
+      )
+
+    }.toMap
+  }
+
+  private def getGeneralizedTimeOfProposalInHours(
+    passenger: PersonIdWithActorRef,
+    proposal: Option[TravelProposal]
+  ): Double = {
+    // TODO: add walking time once walk-to-point service is implemented
+    proposal match {
+      case Some(proposal) =>
+        val wait = proposal.maxWaitingTimeInSec
+        val duration = proposal.travelTimeForCustomer(passenger)
+        (duration + (wait * beamServices.beamConfig.beam.agentsim.agents.modalBehaviors.modeVotMultiplier.waiting)) / 3600
+      case _ => 0.0
+    }
+
   }
 }
 

src/main/scala/beam/sim/config/BeamConfig.scala

@@ -634,6 +634,7 @@ object BeamConfig {
               drive_transit_intercept: scala.Double,
               ride_hail_intercept: scala.Double,
               ride_hail_pooled_intercept: scala.Double,
+              ride_hail_subscription: scala.Double,
               ride_hail_transit_intercept: scala.Double,
               transfer: scala.Double,
               transit_crowding: scala.Double,
@@ -662,6 +663,8 @@ object BeamConfig {
                   ride_hail_pooled_intercept =
                     if (c.hasPathOrNull("ride_hail_pooled_intercept")) c.getDouble("ride_hail_pooled_intercept")
                     else 0.0,
+                  ride_hail_subscription =
+                    if (c.hasPathOrNull("ride_hail_subscription")) c.getDouble("ride_hail_subscription") else 0.0,
                   ride_hail_transit_intercept =
                     if (c.hasPathOrNull("ride_hail_transit_intercept")) c.getDouble("ride_hail_transit_intercept")
                     else 0.0,
@@ -965,6 +968,7 @@ object BeamConfig {
         }
 
         case class RideHail(
+          bestResponseType: java.lang.String,
           cav: BeamConfig.Beam.Agentsim.Agents.RideHail.Cav,
           charging: BeamConfig.Beam.Agentsim.Agents.RideHail.Charging,
           freeSpeedLinkWeightMultiplier: scala.Double,
@@ -1507,6 +1511,8 @@ object BeamConfig {
 
           def apply(c: com.typesafe.config.Config): BeamConfig.Beam.Agentsim.Agents.RideHail = {
             BeamConfig.Beam.Agentsim.Agents.RideHail(
+              bestResponseType =
+                if (c.hasPathOrNull("bestResponseType")) c.getString("bestResponseType") else "MIN_COST",
               cav = BeamConfig.Beam.Agentsim.Agents.RideHail.Cav(
                 if (c.hasPathOrNull("cav")) c.getConfig("cav")
                 else com.typesafe.config.ConfigFactory.parseString("cav{}")

src/test/scala/beam/integration/ridehail/MultipleRideHailManagerSpec.scala

@@ -24,7 +24,7 @@ class MultipleRideHailManagerSpec extends AnyWordSpecLike with Matchers with Bea
         case pte: PathTraversalEvent if pte.vehicleId.toString.startsWith("rideHail") => pte
       }
       val groupedByFleetId = rhPTE.groupBy(pte => getFleetId(pte.vehicleId))
-      groupedByFleetId.keySet shouldBe Set("Uber", "Lyft")
+      groupedByFleetId.keySet shouldBe Set("Uber", "Lyft", "Cruise")
     }
   }
 

test/input/beamville/beam-multiple-rhm.conf

@@ -5,6 +5,8 @@ beam.agentsim.simulationName = "multiplerhm"
 beam.agentsim.firstIteration = 0
 beam.agentsim.lastIteration = 0
 
+beam.agentsim.agents.modalBehaviors.multinomialLogit.params.ride_hail_subscription = 2
+
 beam.debug.messageLogging = true
 beam.debug.maxSimulationStepTimeBeforeConsideredStuckMin = 50
 beam.outputs.events.fileOutputFormats = "csv.gz,xml" # valid options: xml(.gz) , csv(.gz), none - DEFAULT: csv.gz
@@ -13,6 +15,8 @@ beam.physsim.skipPhysSim = true
 
 beam.debug.stuckAgentDetection.enabled = false
 
+beam.agentsim.agents.rideHail.bestResponseType = "MIN_UTILITY"
+
 beam.agentsim.agents.rideHail.managers = [
   {
     name = "Uber"
@@ -59,8 +63,12 @@ beam.agentsim.agents.rideHail.managers = [
     initialization.initType = "FILE"
     # If FILE, use this param
     initialization.filePath = ${beam.inputDirectory}"/rideHailFleet.csv"
-    defaultCostPerMile = 1.25
-    defaultCostPerMinute = 0.75
+    defaultBaseCost = 1.0
+    defaultCostPerMile = 0.5
+    defaultCostPerMinute = 0.14
+    pooledBaseCost = 1.1
+    pooledCostPerMile = 0.6
+    pooledCostPerMinute = 0.01
     rideHailManager.radiusInMeters = 5000
     # allocationManager(DEFAULT_MANAGER | EV_MANAGER | POOLING_ALONSO_MORA)
     allocationManager.name = "POOLING_ALONSO_MORA"
@@ -91,5 +99,47 @@ beam.agentsim.agents.rideHail.managers = [
     allocationManager.repositionLowWaitingTimes.waitingTimeWeight = 4.0
     allocationManager.repositionLowWaitingTimes.demandWeight = 4.0
     allocationManager.repositionLowWaitingTimes.produceDebugImages = true
-  }
+  },
+  {
+      name = "Cruise"
+      iterationStats.timeBinSizeInSec = 3600
+      # Initialization Type(PROCEDURAL | FILE)
+      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)
+      initialization.procedural.initialLocation.name = "HOME"
+      initialization.procedural.initialLocation.home.radiusInMeters = 500
+      initialization.procedural.fractionOfInitialVehicleFleet = 0.5
+      initialization.procedural.vehicleTypeId = "beamVilleCar"
+      defaultBaseCost = 10.0
+      defaultCostPerMile = 0.5
+      defaultCostPerMinute = 0.14
+      pooledBaseCost = 1.1
+      pooledCostPerMile = 0.6
+      pooledCostPerMinute = 0.01
+      rideHailManager.radiusInMeters = 5000
+      # allocationManager(DEFAULT_MANAGER | EV_MANAGER | POOLING_ALONSO_MORA)
+      allocationManager.name = "POOLING_ALONSO_MORA"
+      allocationManager.requestBufferTimeoutInSeconds = 200
+      allocationManager.maxWaitingTimeInSec = 900
+      allocationManager.maxExcessRideTime = 0.5 # up to +50%
+      # repositioningManager can be DEFAULT_REPOSITIONING_MANAGER | DEMAND_FOLLOWING_REPOSITIONING_MANAGER | REPOSITIONING_LOW_WAITING_TIMES | INVERSE_SQUARE_DISTANCE_REPOSITIONING_FACTOR
+      repositioningManager.name = "REPOSITIONING_LOW_WAITING_TIMES"
+      repositioningManager.timeout = 300
+      # REPOSITIONING_LOW_WAITING_TIMES
+      allocationManager.repositionLowWaitingTimes.percentageOfVehiclesToReposition = 1.0
+      allocationManager.repositionLowWaitingTimes.repositionCircleRadiusInMeters = 3000
+      allocationManager.repositionLowWaitingTimes.timeWindowSizeInSecForDecidingAboutRepositioning = 1200
+      allocationManager.repositionLowWaitingTimes.allowIncreasingRadiusIfDemandInRadiusLow = true
+      allocationManager.repositionLowWaitingTimes.minDemandPercentageInRadius = 0.1
+      allocationManager.repositionLowWaitingTimes.minimumNumberOfIdlingVehiclesThresholdForRepositioning = 1
+      # repositioningMethod(TOP_SCORES | KMEANS)
+      allocationManager.repositionLowWaitingTimes.repositioningMethod = "TOP_SCORES"
+      allocationManager.repositionLowWaitingTimes.keepMaxTopNScores = 5
+      allocationManager.repositionLowWaitingTimes.minScoreThresholdForRepositioning = 0.00001
+      allocationManager.repositionLowWaitingTimes.distanceWeight = 0.01
+      allocationManager.repositionLowWaitingTimes.waitingTimeWeight = 4.0
+      allocationManager.repositionLowWaitingTimes.demandWeight = 4.0
+      allocationManager.repositionLowWaitingTimes.produceDebugImages = true
+    }
 ]