Using DTMon to Monitor Transient Flow Traffic

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We evaluate the performance of the DTMon dynamic traffic monitoring system to measure travel times and speeds in transient flow traffic caused by non-recurring congestion. DTMon uses vehicular networks and roadside infrastructure to collect data from passing vehicles. We show DTMon's ability to gather high-quality real-time traffic data such as travel time and speed. These metrics can be used to detect transitions in traffic flow (e.g., caused by congestion) especially where accurate flow rate information is not available. We evaluate the accuracy and latency of DTMon in providing traffic measurements using two different methods of message delivery. We show the advantages of using dynamically-defined measurement points for monitoring transient flow traffic. We compare DTMon with currently in-use probe-based systems (e.g., AVL) and fixed-point sensors and detectors (e.g., ILD).

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Using DTMon to Monitor Transient Flow Traffic

  1. 1. Using Dtmon TO Monitor Transient Flow Traffic<br />Hadi Arbabi<br />and <br />Michele C. Weigle<br />Department Of Computer Science<br />Old Dominion University<br />Second IEEE Vehicular Networking Conference, December 2010, NJ<br />
  2. 2. Motivation<br />Real-time monitoring of traffic <br /><ul><li>Accurate estimation of travel time and speed
  3. 3. Required in transient flow traffic (e.g., congestion)
  4. 4. Fixed point sensors and detectors cannot estimate travel time and space mean speed</li></ul>Trends toward probe vehicle-based systems<br />Dynamic points of interest<br />Augment current technologies<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />2<br />
  5. 5. Content<br />INTRODUCTION<br />Traffic Monitoring<br />Dynamic Traffic Monitoring (DTMon)<br />Task Organizer<br />Vehicles<br />Virtual Strips<br />Methods of Message Delivery <br />APPROACH<br />Monitoring Traffic Data in Rural Areas<br />Highways<br />EVALUATION<br />Transient Flow Traffic<br />SUMMARY<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />3<br />
  6. 6. Introduction<br />Monitoring<br />Vehicle classification<br />Count information<br />Flow rate<br />Volume<br />Density<br />Traffic speed<br />Time mean speed (TMS)<br />Space mean speed (SMS)<br />Travel time (TT)<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />4<br />Traffic Management Center (TMC)<br />
  7. 7. Technologies In Use<br />Fixed point sensor and detectors<br />Inductive loop detectors (ILD)<br />Acoustic sensors<br />Microwave radar sensors<br />Video cameras<br />Probe vehicle-based system<br />Automatic vehicle location (AVL)<br />Wireless location technology (WLT)<br />Automatic vehicle identification (AVI)<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />5<br />
  8. 8. Dynamic Traffic Monitoring (DTMon)<br />DTMon - A probe vehicle-based system using VANET and dynamically defined points of interest on the roads<br />Task Organizers (TOs)<br />Vehicles<br />Virtual Strips (VS)<br />Imaginary lines or points<br />Methods of Message Delivery<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />6<br />
  9. 9. DTMon: Task Organizer & Virtual Strips<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />7<br />Virtual Strip<br />TO<br />Virtual Segment<br />Virtual Strip<br />
  10. 10. Task Organizer (TO)<br />Communicates with passing vehicles <br />Assigns measurement tasks<br />Collects reports from the vehicles<br />Organizes received measurements<br />Informs upcoming traffic conditions<br />Multiple TOs<br />Centralized<br />Aggregate information about the whole region<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />8<br />
  11. 11. Vehicles<br />Equipped<br />GPS and DSRC communications device<br />CPU and Required Applications<br />Record<br />Speed<br />GPS Position<br />Travel Direction<br />Timestamp<br />Classification, Route Number, and …<br />Receive tasks from a TO<br />Triggered at a specific time, speed, or location<br />Report<br />Forwarded to the listed TOs<br />Stored and carried to the next available TO<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />9<br />
  12. 12. Multiple TOs Multiple VS<br />Multiple VS and Segments<br />Dynamically Defined<br />Multiple TOs<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />10<br />A Sample Task From TO to Vehicles<br />
  13. 13. Methods of Message Delivery<br />Regular Forwarding (RF) <br />Store-and-Carry (SAC) [if multiple TOs]<br />Hybrid<br />RF+SAC<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />11<br />
  14. 14. Evaluation<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />12<br />Several experiments using VANET modules that we developed for the ns-3 simulator <br /><ul><li>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
  15. 15. Highway Mobility for Vehicular Networks (Project and Google Code)
  16. 16. http://code.google.com/p/ns-3-highway-mobility/</li></li></ul><li>(Overview)<br />In our previous work<br />Message Reception<br />Effect of Traffic Density, Flow Rate, Speed<br />Effect of Market Penetration Rate <br />Effect of Transmission Range<br />Effect of Traffic In Opposite Direction<br />Distance From TOs<br />Latency and Message Delay<br />Comparison among Methods of Message Delivery <br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />13<br />Hadi Arbabi and Michele C. Weigle, “Monitoring Free-Flow Traffic using Vehicular Networks,” In Proceedings of the IEEE Intelligent Vehicular Communications System Workshop (IVCS). Las Vegas, NV, January 2011<br />
  17. 17. Evaluation<br />Factors that can affect the Quality of Data<br />Market penetration rate (PR) <br />Method of Message Delivery<br />Message Reception Rate (MRR)<br />Information Reception Rate (IRR)<br />IRR ≈ MRR x PR<br />Latency and Message Delay<br />Methods that can collect more informationfrom vehicles with less latencyare preferred in up-to-date traffic monitoring<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />14<br />
  18. 18. Simulation Setup<br />Bi-directional four-lane highway<br /> TO1 is located at 1 km away <br />TO5 is located at 5 km away (optional secondary TO)<br />Vehicles enter the highway with<br /> Medium flow rate (average 1800 veh/h) <br />Uniform Distribution<br />Desired speed 65±5 mph (29±2.2 m/s)<br />Normal Distribution<br />20% of vehicles are Truck <br />Uniform Distribution<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />15<br />
  19. 19. Simulation Setup<br />Non-recurring congestion (5 min stoppage)<br />Transient Flow Traffic<br />Stopping a vehicle in the first lane after 5 minutes for 5 minutes<br />Between VS1 and VS2 outside the communication range (300 m) of TO1<br />Stopped vehicle starts moving, allowing traffic flow to gradually return to normal<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />16<br />
  20. 20. Comparison<br />The performance of DTMon compared with<br /> Actual simulation status (ground truth) <br />Fixed point sensors and detectors<br />Actual simulation data sampled from VS1 and VS2<br />AVL<br />Equipped Trucks<br />10 runs of the simulation (20 min each) for each experiment<br />Test with penetration rates of 5, 10, 25, 50, and 100%<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />17<br />
  21. 21. MRR and IRR<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />18<br />Message Reception:<br />RF+SAC > RF<br />MRR<br />7%<br />15%<br />IRR<br />
  22. 22. MRR<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />19<br />RF + SAC = RF + Rest<br />Higher Penetration = Higher RF = Less Delay<br />VS2<br />
  23. 23. Estimated Travel Time (ILDs vs. Actual)<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />20<br />Fixed Point Sensor and Detector’s Poor Estimation of TT and SMS<br />
  24. 24. Travel Time<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />21<br />Quality of Data<br />RF+SAC >= RF > AVL<br />VS2<br />VS2<br />
  25. 25. Space Mean Speed (SMS)<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />22<br />VS2<br />VS2<br />
  26. 26. Flow Rate<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />23<br />Count Information (e.g., Flow Rate and Volume)<br />Only in High PR<br />VS2<br />
  27. 27. Message Delay<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />24<br />RF Delay Very Low<br />TO1<br />VS2<br />TO5<br />RF+SAC Delay <br />1. Amount of Carried Messages<br />2. TT<br />More RF<br />Less Delay<br />More SAC <br />More Delay<br />
  28. 28. Quality of Data<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />25<br />t-test Alpha = 0.05 (Confidence > 95%)<br />
  29. 29. Quality of Data<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />26<br />t-test Alpha = 0.05 Confidence > 95%<br />
  30. 30. Summary<br />DTMoncan estimate good quality Travel Time and Speed<br />DTMoncan detect transition in traffic flow using estimated Travel Time and Speed<br />DTMoncan estimate good quality flow rate and density in higher penetration rates<br />RF and RF+SAC have similar performance in higher penetration rates<br />Using RF+SAC is an improving option in low penetration rates<br />DTMoncan augment current technologies and monitoring systems<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />27<br />
  31. 31. Questions?<br />Hadi Arbabi and Michele C. Weigle<br />Department of Computer Science at Old Dominion University<br />Vehicular Networks, Sensor Networks, and Internet Traffic Research<br />http://oducs-networking.blogspot.com/<br />{marbabi, mweigle}@cs.odu.edu<br />Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu<br />28<br />This work was supported in part by the National Science Foundation under grant CNS-0721586.<br />

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