ITS: Urban Mobility · DFROUTER SUMO-routes induction-loop measures JTRROUTER SUMO-routes turning...
Transcript of ITS: Urban Mobility · DFROUTER SUMO-routes induction-loop measures JTRROUTER SUMO-routes turning...
ITS:UrbanMobility
Contents
• Reasonsforanopen-sourcetrafficsimulation• Neededmodels,data,anditsprocessing• SUMOOverview
OpenSourceRoadTrafficSimulationApplicationsforaroadtraffic
simulation• reproducible,computerizedevaluationof
– real-worldnetworkperformance– newconceptsfor
• moderntrafficsignalcontrol• trafficsurveillance• trafficforecasting• trafficmanagement• dynamicroutingmethods• car2car/car2infrastructurecommunication
– trafficmodels• instructivevisualization• planning
OpenSourceRoadTrafficSimulationNeededComponents
road network vehicles / flow signalling
OpenSourceRoadTrafficSimulationWhyanopensourceroadtraffic
simulation?Commonprocedure• Anacademicorganizationwantstoevaluateanaspectoftraffic• Buildsanowntrafficsimulation;needs
– Anetworkrepresentationincludingspeedlimits,right-of-way– rules,etc.
– Arepresentationofvehicles,theirmovement,routes,etc.
– Arepresentationofsignals,variablemessagesigns,etc.
…yieldsinMany(incomplete)simulationswhichresultscannotbecomparedà Solution:anextendablesimulationasabaseforowndevelopment
OpenSourceRoadTrafficSimulationDesigncriteria
• Portability– Achievedbyusingc++andportablelibrariesonly
• Performance– Highexecutionspeed– Nolimitationsinnetworksizeandnumberofsimulatedvehicles
• Extendibility• Opensource
– LicensedunderGPL– Hostedatsourceforge(http://sumo.sourceforge.net)
• Microscopic– Eachvehicleismodeledexplicitly
RoadTrafficSimulationRoadTrafficFlowDynamics(oneof)
Fundamental diagram of traffic (counted through induction loops)
As interpreted by Kerner As interpreted by Kim and Keller
RoadTrafficFlowSimulationClasses
macroscopic microscopic sub-microscopic
mesoscopic
RoadTrafficFlowSimulationMicroscopicModels
Mostly:• discreteintime• “CarFollowingModels”:
vehicle‘sspeeddependsontheprecedingvehicle
RoadTrafficFlowSimulationCarFollowingModelbyKrauß*
Features:• continuousinspace• discreteintime• accident-free• stochasticdrivermodel
Parameters:accelerationdecelerationmax. speeddriver’s imperfection
[*] “Microscopic Modelling of Traffic Flow: Investigation of Collision Free Vehicle Dynamics”, S. Krauß, DLR (Hauptabteilung Mobilität und Systemtechnik), 1998, ISSN 1434-8454
RoadTrafficFlowSimulationFurtherModelsneeded
• LaneChanging– Navigation(Ihavetoturnleftonnextjunction)– Tactical(leftlaneallowsmetomovefaster)
• Interactionwiththenetwork– Stopsatredtrafficlights– Decelerateifothervehicleshaveahigherrighttomoveon
thenextjunction• Extensions
– Vehicleclasses,suchasbusses,whichuseownlanes– Busstops
MicroscopicRoadNetworksNeededvs.givenInformation
Neededstreetattributes:• Numberoflanes• Allowedspeed• Per-lanerestrictions• Allowedcontinuations
Neededjunctionattributes:• Right-of-way• Trafficlightsprogram
Given: a graph with only few informationNumber of lanes (often vague)Allowed speed (often vague)
MicroscopicRoadNetworksSizeandComplexity
Additionalproblems:• verylargenetworks• complexjunctions
Example: computation of lane-to-lane connections:
1. for each edge: compute turnaround edges2. for each node: sort each node’s edges3. for each node: compute each node’s type4. for each node: set edge priorities5. for each edge: compute edge-to-edge
connections6. for each edge: compute lanes-to-edge
connections7. for each node: compute lane-to-lane
connections8. for each edge: recheck lanes9. for each edge: append turnarounds
MicroscopicRoadNetworksSUMOSolution:Heuristicsfor
automaticcomputationAfter 2. After 5.
After 7. After all
TrafficDemandNeededInformation
Eachvehicleismodeledexplicitly
mandatoryattributes:
– ID(name)– Vehicletype(includingKrauß-parameter)– Completeroutethroughthenetwork– Departtime
optionalattributes:
– stops<vehicle id="bus100_west_0d" type="BUS" depart="0" color="1,0,1"><route>-572658025 -572658026 -572658027 -572658024 …</route><stop bus_stop="west1" duration="20"/><stop bus_stop="west2" duration="20"/>
</vehicle>
TrafficDemandPossibleSources
– Accuratebutsparse– Noinformationaboutwhoisdrivingfromwhichplacetowhichplace,onlyhowmanyvehicleshavepassedacertainplace
– Lessaccuratethaninductionloopsandsparse,too–mainlyforsomemajorinner-cityjunctions
– Informationaboutflowspreadonjunctions
– Evenlessaccurate(estimated),butcoveracompletearea
Real (physical) measures are done at induction loops
Counts at junctions may provide flows and turning ratios
Traffic scientists estimate demands on district level
TrafficDemandPossibleSources
DFROUTER SUMO-routesinduction-loopmeasures
JTRROUTER SUMO-routesturning ratios
flows
OD2TRIPS
trips
OD-matrix
DUAROUTER SUMO-routes
Real (physical) measures are done at induction loops
Counts at junctions may provide flows and turning ratios
Traffic scientists estimate demands on district level
SUMO- ModulesIncludedApplications
• SUMO:commandlinesimulation
• GUISIM:simulationwithagraphicaluserinterface
• NETCONVERT:networkimporter
• NETGEN:networkgenerator• OD2TRIPS:converterforO/D-
matrices• JTRROUTER:routerbasedon
turningratios• DUAROUTER:routerbasedona
dynamicuserassignment• DFROUTER:routerwhichuses
detectordata
SUMOFeatures
• Microscopic:allvehiclesaremodeledexplicitly• Time-discrete,space-continuouscar-followingmodelbyS.Krauß• Multi-lanetraffic,right-of-wayrules• Around100,000carsinreal-time(withoutgraphicaloutput)• Trafficlightswithtimeschedules,othertrafficmanagementdevices• Complexnetworks/Importsupport• DynamicRoutingbasedonDynamicUserAssignment• Otherroutingmodules
[2] Christian Gawron. 1998. “Simulation-Based Traffic Assignment”. Inaugural Dissertation.
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[1] “Microscopic Modelling of Traffic Flow: Investigation of Collision Free Vehicle Dynamics”, S. Krauß, DLR (Hauptabteilung Mobilität und Systemtechnik), 1998, ISSN 1434-8454
ProjectsattheDLRwhereSUMOwasused
2002-2006• INVENTImplementationandverificationoftrafficmanagementstrategiesforlargeurbanareas
• OISVerificationofbenefitsarisingfromusageofnewopticalsensors
• TrafficTowerVirtualTrafficManagementEnvironment
• WJT2005/Soccer2006Integrationofstandardandairbornedetectorsintoatrafficportalwithforecastfunctionality
• TrafficOnlineTrafficsurveillanceviain-vehicleGSMphones
WJT2005/Soccer2006à DELPHIDescription
• UsedinthecityofCologneduring– Thepope’svisit(worldyouthday2005)– Theworldsoccercup2006
• Trafficsurveillanceusing– Highwayinductionloops– Inner-cityinductionloops– Airbornetrafficrecognitionsystem(mountedonazeppelin)
• Trafficvisualization– Integrationofgainedinformationintoaviewer– Presentationforthepolice
• Trafficforecast– 30minintothefutureusingasimulation(extendedSUMO)
DELPHISystemOverview
Floating Car Data:Induction loops:ARGOS:Access via db, webserver
Simulation:
• Areal extrapolation of measured data
• Forecast generation (30min)
• Fed from db using python
Datenbank:
• MySQL with InnoDB tables
• No built-in logic
Aggregation/Correction:
• Guessing missing values
• Data fusion• Done using python
scripts
Webserver:• Tomcat Servlet Container• Visualization (Images and traffic)
Webbrowser:• JavaScript Browser• asynchronous XML-
Requests
SUMO– ExampleResultsFlowComparison
CaseStudy
• Motivation:– Constraintsonaccessingthemobileusersrelateddata
– Producingthelogs- enablingnewpossibilitiesintheresearchfield.
– Vehicle-To-Everything(V2X)communication
IntroducingCellularNetworkLayerintoSUMOforSimulatingVehicularMobileDevices’InteractionsinUrbanEnvironment
*Siim-Toomas,Marran;Artjom, Lind;Amnir,Hadachi,“Introducing Cellular NetworkLayerintoSUMOforSimulatingVehicular MobileDevices'Interactions inUrbanEnvironment”, The4thInternational Conference onVehicleTechnologyandIntelligent Transport SystemsandControl Systems,Madeira,Portugal,March,2018.
CellularNetwork
• Basestation• Basestationtransmitter• Cells- signalcoverage• Userequipment- mobile
MobilityData
• Mobiletechnologies1Gto5G• Mobilitydata:
– CDR- likelogs– GPSdata– RFID– Wirelessaccesspointrelatedlogs– etc
Manyunstandardizedtechnologies
ChallengesinanalyzingCDRlogs
• SparseEvents• UnreliableCoverageMap• ChangesinCoverageMap• Featuresof2G/3G/4Gtechnologies
Contribution
• Researchofthemicroscopicroadtrafficsimulatorandcellularnetworktechnologiestodesignandmodelcellularnetwork
• Integrationofthecellularnetworkentitiesintotheroadtrafficsimulationsoftware
• Runningcommand-lineandvisualizetrafficsimulationwithintegratedcellularnetworksimulation
Contribution
Contribution:Supportprgrams
• HexagonGen• MobilityEventSimulationGenerator-MESGEN
Contribution:Classes• GUIandmicrosim:• Cellularantenna• Signalpropagation
• Cellulartower• Cellulartowercontroller• MobileandGPSdevices• MobileEventData• Dataimportandexport
Results:GPS
• Mobilemobilitylogs– GPSdata
Results:CDR
• Mobilemobilitylogs– CDR-like
• Eventtypes:– Callinitiation– Callreceiving– SMSsending– SMSreceiving– Webcommunication
Results:GUI
• Visualization:
SUMOTasksToDo
• Checkthereadingssectionincoursewebpage• Downloadandinstalltherightversionofsumoonyourcomputer.
• Trytorunittobereadyforthelabsession