Präsentation zum Thema: "V2X Simulation and Modeling Martin Treiber TU Dresden"— Präsentation transkript:
1V2X Simulation and Modeling Martin Treiber TU Dresden Oct 2010, Dagstuhl
2General: About what I will talk – and about what not ModelsLevel of DetailApplicationsRay tracing/wave propagationNetworking models (NSx)Path modelsCar-following modelsFluid-dynamic modelsPhysical detailsPacket transmission detailsTransmission strategyMicroscopic traffic flowMacroscopic traffic flowSafetyEfficiencyEntertainment
3Longitudinal communication path OverviewGeneralLongitudinal communication path- Assumptions and analytical models- Integration in a microscopic traffic simulator- Results: Which minimum equipment rate is necessary?Transversal (store-and forward) communication path- Analytical models- Simulations and minimum equipment rates- Discussion: Which strategy for which application?Applications for enhancing efficiency- Congestion warning system (V2V / I2V)- Traffic flow assistant (V2V / I2V)- Traffic light assistant (I2V)
4Transmission Strategy: V2Vlong, V2Vtrans, V2I, and I2V
5Longitudinal Communication Path: Assumptions Direction of communicationDriving directionFinite broadcast range r: Either fixed or statistically distributedVehicle density ρ: Either constant or inhomogeneous (stop-and-go traffic)Constant partial density of equipped vehicles: λ=αρPoisson-process for the positions of “nodes”, i.e, distances between nodes are exponentially distributedInstantaneous, error-free message transmission, if within range
6Longitudinal Communication Path: Analytical Model Poissonian assumption:f(y)=Probability of availability at distance x from sender:prob densityavail. at x-yInitial condition:Closed-form analytical solution:Dousse, Thiran, Hasler, Connectivity in Ad Hoc and Hybrid Networks. IEEE INFOCOM Vol. 2, pages (2002).
7Predictions of the Analytical Model Variable rangeVariable equipment rateactualPotentialLength of communication chainchainResult: Longitudinal strategy is not efficient as long as broadcast range R is lower than average distance 1/λlength
8Test of Fixed Range Assumption: Deterministic vs Test of Fixed Range Assumption: Deterministic vs. Stochastic Range Model
9Robustness Test 1: Deterministic Analytical Model vs Robustness Test 1: Deterministic Analytical Model vs. Simulated Trajectories (Gipps Model)Nearly perfect agreement for free traffic and rates below 30% => small lSimulated connectivity slightly higher for homogeneous congested trafficSimulated connectivity slightly lower for longer distances and stop&go traffic
10Robustness Test 2: Analytical Model vs. Real Trajectories (NGSIM Data) First 20 minJamSpatiotemporal DensityReal gap distribution (across all lanes)Free trafficLast 10 min (jam)First 20 minCrossover!
11Transverse Communication Path: „Store & Forward“ Communication Connectivity Statistics of Store-and-Forward Intervehicle Communication, IEEE Transactions on Intelligent Transportation Systems 11(1), (2010).
12Analytical Model for Store & Forward Communication Problem statement: Message has to propagate in upstream direction (at least) distance xTime T1 to find a relay car:Time T2 to reach connectivity to the target region:t*=(x-2r2)/V2Time T3 to find a car in the target region:Schönhof, Kesting, Treiber and Helbing,Coupled vehicle and information flows:Message transport on a dynamic network.Physica A 363, p
13Predictions of the Analytical Deterministic Model Transversal hopping has robust connectivity already for small penetration rate α, i.e., small partial density l=raIA successful transmission is only a matter of timeFlip side: The transmission is not instantaneousr1=r2=30 Veh./km, V1=V2=90 km/h, 1 lane
14Robustness Test 1: Deterministic vs. Stochastic Model Deterministic: Fixed range R=200 mStochastic: Exponentially distributed range, E(R)=200 mr1=r2=30 Veh./km, V1=V2=90 km/h, 1 lane
15Robustness Test 2: Violation of the Poissonian Assumption (for 1 and 2 Lanes) Solid lines: Analytial deterministic modelSymbols: IDM/MOBIL microsimulations
16Implementation and simulation of inter-vehicle communication Message: Data structureEquippedVehicle: Mailbox with send() and receive()MessagePool: “Ether” for gathering and distributing MessagesSimulate!
17Applications for Enhancing Efficiency: Problem Statement Capacity of road network is limitedConstruction of new roads not feasible due to cost/land constraintsHow can we avoid / reduce traffic congestion ?Vehicle-based Intelligent Transportation Systems !17
18Application 1: Congestion warning system (V2Vtrans)
19Basic Communication Scheme Scenario: Evolution of congested trafficTrajectories of equipped cars (α=3%)Equipped vehicles generate messagesOnline traffic state estimation based on floating car data.To appear in Traffic and Granular Flow '09, Springer (2009).
20Enhancing the Jam Front Prediction Using V2I, I2I, I2V RSU downRSU up
22ACC-Based Traffic- Flow Assistant Application 2:ACC-Based Traffic- Flow AssistantWorld 2-3 fahrzeugeKesting, Treiber, Helbing : Enhanced Intelligent Driver Model to Access the Impact of Driving Strategies on Traffic Capacity. Philosophical Transactions of the Royal Society A (2010).2222
23(1) The “Ordinary” ACCThe ModelThe SystemWorld 2-3 fahrzeuge2323
24(2) From Conventional to Traffic-Efficient ACC Automated “traffic-efficient” driving strategyStrategy adapts driving style to surrounding traffic situationDo not change model but model parametersDriving strategy matrix in relative terms (preserving individual settings)Kesting, Treiber, Schönhof, Helbing : Adaptive Cruise Control Design for Active Congestion Avoidance. Transportation Research Part C: Emerging Technologies (2008).2424
25Traffic-Adaptive Driving Strategy in Free Traffic Driving strategy for free traffic and bottleneck situations:Traffic stateDriving behaviorλTλaλbFree trafficDefault1BottleneckBreakdown prevention0.71.5Bottleneck section: avoid traffic flow breakdownReduce time gap parameter and increase stabilityDynamically “fill-up” capacity gap Increase free capacityTreiber, Kesting, Helbing: Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts. Transportation Research Part B: Methodological (2010).2525
26Driving Strategy in Congested Traffic Driving in congested traffic:Safely approaching jam frontIncrease outflow by quickly accelerating when leaving traffic jamTraffic stateDriving behaviorλTλaλbUpstream jam frontIncreased safety10.7Congested trafficDefault / NormalDownstream jam frontIncrease outflow0.52Typical sequence of traffic states for a floating car :2626
27Spatial-Temporal Dynamics of a Freeway Traffic Jam Problem: How to distinguish exiting just a stop & go wave from exiting the whole jammed region?Solution: Knowledge of Traffic flow dynamics and C2X communication!Treiber, Kesting, Wilson: Reconstructing the Traffic State by Fusion of Heterogeneous Data. Computer-Aided Civil and Infrastructure Engineering (2010).2727
31Simulation Results: Impact on Collective Travel Time and Fuel Consumption Averaging Problem!
32“Traffic-Light Assistant” in City Traffic Application 3:“Traffic-Light Assistant” in City TrafficFirst Step: Driver information systemTraffic light reports about statusWireless communication to carsCars give recommendations to driverex: “Travolution” by Audi et al.Next step: Automatic drivingStop line additional “virtual” targetIn combination with ACC systemAims: Convenience/Safety/Efficiency3232
36Protection and Prevention is a Success Story German statistics over 40 years:Mileage nearly tripledFatalities reduced by 75% !Similar developments in all western countries3636
37In which way can V2V/V2I make cars smarter? Anti-lock Brake System (ABS)AirbagsElectronic Stability Control (ESC)Passenger ProtectionPedestrian ProtectionAdaptive headlightsNavigationVehicle- Infrastructure Integration (VII)Radar-based systemsUltrasonic-based systemsCar-to-car communication (C2C)Video-based systems3737
38Stylized Facts of Spatiotemporal Evolution (A9 South, Germany) Downstream front: Either fixed or moving upstream with velocity cUpstream front: Determined by supply and demandInternal structures: Moving with velocity c as wellAmplitude of internal structures grows when moving upstreamFrequency grows with severety of bottleneckTSGOCT
39Summary: From Driver- to “Traffic-Assistance” Systems Required InformationActionAimACCTrack leading vehicleControl accelerationDriver comfortTraffic-adaptive ACCBottlenecks and jam frontsACC parameter adaptation“Flow-efficient” driving strategyTraffic-Light AssistantTraffic light phasesAutomatic approach of TL“Stop-avoiding” driving strategyDemonstration of automated driving strategies:Implementation in test cars by VolkswagenPositive impact on collective dynamics in simulationIndividual benefit from additional driver information3939