![Message ReceptionHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu12B = inter-vehicle spacingp = penetration rateS = mean speedv = flow rateE = inter-vehicle spacing of equipped vehiclesR0 = transmission range d = distance to TOE[C] = expected inter-vehicle spacing](https://image.slidesharecdn.com/dtmon-ivcs-110114113317-phpapp01/85/Using-DTMon-to-Monitor-Free-Flow-Traffic-14-320.jpg)
Using DTMon to Monitor Free Flow Traffic
- 1. Hadi Arbabiand Michele C. WeigleDepartment Of Computer ScienceOld Dominion UniversityMonitoring FREE FLOW TRAFFIC USING VEHICULAR NETWORKs3rd IEEE Intelligent Vehicular Communications System Workshop (IVCS), January 2001, Las Vegas
- 2. MotivationReal-time monitoring of traffic Accurate estimation of travel time and speed
- 3. Including accidents, work zones, and other potential causes of congestion
- 4. Fixed point sensors and detectors cannot estimate travel time and space mean speedTrends toward probe vehicle-based systemsDynamic points of interestAugment current technologiesEffect of Market Penetration RateHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu2
- 5. ContentINTRODUCTIONTraffic MonitoringDynamic Traffic Monitoring (DTMon)Task OrganizerVehiclesVirtual Strips APPROACHMonitoring Traffic Data in Rural AreasHighwaysMessage ReceptionMethods of Message DeliveryEVALUATIONFree-Flow TrafficSUMMARYHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu3
- 6. IntroductionMonitoringVehicle classificationCount informationFlow rateVolumeDensityTraffic speedTime mean speed (TMS)Space mean speed (SMS)Travel time (TT)Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu4Traffic Management Center (TMC)
- 7. Technologies In UseFixed point sensor and detectorsInductive loop detectors (ILD)Acoustic sensorsMicrowave radar sensorsVideo camerasProbe vehicle-based systemAutomatic vehicle location (AVL)Wireless location technology (WLT)Automatic vehicle identification (AVI)Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu5
- 8. Dynamic Traffic Monitoring (DTMon)DTMon - A probe vehicle-based system using VANET and dynamically defined points of interest on the roadsTask Organizers (TOs)VehiclesVirtual Strips (VS)Imaginary lines or pointsMethods of Message DeliveryHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu6
- 9. DTMon: Task Organizer & Virtual StripsHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu7Virtual StripTOVirtual SegmentVirtual Strip
- 10. Task Organizer (TO)Communicates with passing vehicles Assigns measurement tasksCollects reports from the vehiclesOrganizes received measurementsInforms upcoming traffic conditionsMultiple TOsCentralizedAggregate information about the whole regionHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu8
- 11. VehiclesEquippedGPS and DSRC communications deviceCPU and Required ApplicationsRecordSpeedGPS PositionTravel DirectionTimestampClassification, Route Number, and …Receive tasks from a TOTriggered at a specific time, speed, or locationReportForwarded to the listed TOsStored and carried to the next available TOHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu9
- 12. Multiple TOsMultiple VSMultiple VS and SegmentsDynamically DefinedMultiple TOsHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu10A Sample Task From TO to Vehicles
- 13. Message Reception (Analysis)Amount of Information Delivered to TOMessage Reception Rate (MRR)Information Reception Rate (IRR)Analyze Various Traffic Characteristics Traffic Speed, Density, Flow RateInter-Vehicle SpacingEquipped VehiclesMarket Penetration Rate (PR)Distance to TOTransmission RangeHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu11
- 14. Message ReceptionHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu12B = inter-vehicle spacingp = penetration rateS = mean speedv = flow rateE = inter-vehicle spacing of equipped vehiclesR0 = transmission range d = distance to TOE[C] = expected inter-vehicle spacing
- 15. What Message Delivery Method?Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu13Flow Rate180036005400veh/hTransmission Range
- 16. Methods of Message DeliveryRegular Forwarding (RF)Dynamic Transmission Range (DTR)Store-and-Carry (SAC)If Multiple TOsHybridRF+SACDTR+SACHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu14
- 17. EvaluationCompare Delivery MethodsPenetration Rate (PR)Message Reception Rate (MRR)Information Reception Rate (IRR)IRR ≈ MRR x PRMessage DelayQuality of Traffic dataDelivery Methods and Type of DataHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu15
- 18. EvaluationHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu16Several experiments using VANET modules that we developed for the ns-3 simulator H. Arbabi, M. C. Weigle, "Highway Mobility and Vehicular Ad-Hoc Networks in ns-3," In Proc. of the Winter Simulation Conference. Baltimore, MD, December 2010
- 19. Highway Mobility for Vehicular Networks (Project and Google Code)
- 20. http://code.google.com/p/ns-3-highway-mobility/Simulation SetupBi-directional six-lane highway TO1 is located at 1 km away TO5 is located at 5 km away (optional secondary TO)Vehicles enter the highway with Medium flow rate (average 1800 veh/h) Uniform DistributionDesired speed 110±18 km/h (30±5 m/s)Normal DistributionFree flow traffic with poor connectivityHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu17
- 21. Simulation Setup10 runs, 30 min each, p {5%, 25%, 50%, 100%}Major defined strips by TOs {VS1 , VS2 , VS5 , VS9}ComparisonEach method with the othersActual simulation (ground truth) dataHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu18
- 22. FreceptionHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu19Higher Penetration = Higher RFFarther Distance= Lower RF
- 23. MRRHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu20Hybrid = Forwarding + Carrying = Full MRRHigher Penetration = More Forwarding = Less CarryingVS250%
- 24. MRRHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu21Higher Distance Does Not Affect SAC or HybridHigher Distance = No ForwardingLower Distance = Higher ForwardingVS250%
- 25. MRR and Traffic In Opposite DirectionHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu2220-25% 20-25%
- 26. Message DelayHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu23RF Delay Very LowHybrid Delay 1. Amount of Carried Messages2. TTMore ForwardingLess DelayMore SAC More Delay
- 27. Quality of DataHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu24t-test Alpha = 0.05 (Confidence > 95%)
- 28. Quality of DataHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu25t-test Alpha = 0.05 Confidence > 95%
- 29. SummaryDTMoncan estimate good quality Travel Time and SpeedDTMoncan estimate good quality flow rate and density in higher penetration ratesHybrid message delivery improves information reception rate with cost of latency as an option for low penetration ratesDTMoncan augment current technologies and monitoring systemsHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu26
- 30. Questions?Hadi Arbabi and Michele C. WeigleDepartment of Computer Science at Old Dominion UniversityVehicular Networks, Sensor Networks, and Internet Traffic Researchhttp://oducs-networking.blogspot.com/{marbabi, mweigle}@cs.odu.eduHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu27This work was supported in part by the National Science Foundation under grant CNS-0721586.