The document proposes a system called DTMon that uses probe vehicles and vehicular ad-hoc networks to monitor transient traffic flow in real-time. DTMon uses task organizers and virtual strips to collect speed, travel time and other traffic data from equipped vehicles with sensors. Evaluation shows DTMon can accurately estimate travel time and speed, including during congestion events, and provide higher quality data than fixed sensors when vehicle penetration rates are sufficient. The hybrid delivery method of regular forwarding and store-and-carry performs best, especially at lower penetration rates.

1. Using Dtmon TO Monitor Transient Flow Traffic Hadi Arbabi and Michele C. Weigle Department Of Computer Science Old Dominion University Second IEEE Vehicular Networking Conference, December 2010, NJ

3. Required in transient flow traffic (e.g., congestion)

4. Fixed point sensors and detectors cannot estimate travel time and space mean speedTrends toward probe vehicle-based systems Dynamic points of interest Augment current technologies Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 2

5. Content INTRODUCTION Traffic Monitoring Dynamic Traffic Monitoring (DTMon) Task Organizer Vehicles Virtual Strips Methods of Message Delivery APPROACH Monitoring Traffic Data in Rural Areas Highways EVALUATION Transient Flow Traffic SUMMARY Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 3

6. Introduction Monitoring Vehicle classification Count information Flow rate Volume Density Traffic speed Time mean speed (TMS) Space mean speed (SMS) Travel time (TT) Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 4 Traffic Management Center (TMC)

7. Technologies In Use Fixed point sensor and detectors Inductive loop detectors (ILD) Acoustic sensors Microwave radar sensors Video cameras Probe vehicle-based system Automatic vehicle location (AVL) Wireless location technology (WLT) Automatic vehicle identification (AVI) Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 5

8. Dynamic Traffic Monitoring (DTMon) DTMon - A probe vehicle-based system using VANET and dynamically defined points of interest on the roads Task Organizers (TOs) Vehicles Virtual Strips (VS) Imaginary lines or points Methods of Message Delivery Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 6

9. DTMon: Task Organizer & Virtual Strips Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 7 Virtual Strip TO Virtual Segment Virtual Strip

10. Task Organizer (TO) Communicates with passing vehicles Assigns measurement tasks Collects reports from the vehicles Organizes received measurements Informs upcoming traffic conditions Multiple TOs Centralized Aggregate information about the whole region Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 8

11. Vehicles Equipped GPS and DSRC communications device CPU and Required Applications Record Speed GPS Position Travel Direction Timestamp Classification, Route Number, and … Receive tasks from a TO Triggered at a specific time, speed, or location Report Forwarded to the listed TOs Stored and carried to the next available TO Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 9

12. Multiple TOs Multiple VS Multiple VS and Segments Dynamically Defined Multiple TOs Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 10 A Sample Task From TO to Vehicles

13. Methods of Message Delivery Regular Forwarding (RF) Store-and-Carry (SAC) [if multiple TOs] Hybrid RF+SAC Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 11

15. Highway Mobility for Vehicular Networks (Project and Google Code)

17. Evaluation Factors that can affect the Quality of Data Market penetration rate (PR) Method of Message Delivery Message Reception Rate (MRR) Information Reception Rate (IRR) IRR ≈ MRR x PR Latency and Message Delay Methods that can collect more informationfrom vehicles with less latencyare preferred in up-to-date traffic monitoring Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 14

18. Simulation Setup Bi-directional four-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 Distribution Desired speed 65±5 mph (29±2.2 m/s) Normal Distribution 20% of vehicles are Truck Uniform Distribution Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 15

19. Simulation Setup Non-recurring congestion (5 min stoppage) Transient Flow Traffic Stopping a vehicle in the first lane after 5 minutes for 5 minutes Between VS1 and VS2 outside the communication range (300 m) of TO1 Stopped vehicle starts moving, allowing traffic flow to gradually return to normal Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 16

20. Comparison The performance of DTMon compared with Actual simulation status (ground truth) Fixed point sensors and detectors Actual simulation data sampled from VS1 and VS2 AVL Equipped Trucks 10 runs of the simulation (20 min each) for each experiment Test with penetration rates of 5, 10, 25, 50, and 100% Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 17

21. MRR and IRR Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 18 Message Reception: RF+SAC > RF MRR 7% 15% IRR

22. MRR Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 19 RF + SAC = RF + Rest Higher Penetration = Higher RF = Less Delay VS2

23. Estimated Travel Time (ILDs vs. Actual) Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 20 Fixed Point Sensor and Detector’s Poor Estimation of TT and SMS

24. Travel Time Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 21 Quality of Data RF+SAC >= RF > AVL VS2 VS2

25. Space Mean Speed (SMS) Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 22 VS2 VS2

26. Flow Rate Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 23 Count Information (e.g., Flow Rate and Volume) Only in High PR VS2

27. Message Delay Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 24 RF Delay Very Low TO1 VS2 TO5 RF+SAC Delay 1. Amount of Carried Messages 2. TT More RF Less Delay More SAC More Delay

28. Quality of Data Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 25 t-test Alpha = 0.05 (Confidence > 95%)

29. Quality of Data Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 26 t-test Alpha = 0.05 Confidence > 95%

30. Summary DTMoncan estimate good quality Travel Time and Speed DTMoncan detect transition in traffic flow using estimated Travel Time and Speed DTMoncan estimate good quality flow rate and density in higher penetration rates RF and RF+SAC have similar performance in higher penetration rates Using RF+SAC is an improving option in low penetration rates DTMoncan augment current technologies and monitoring systems Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 27

31. Questions? Hadi Arbabi and Michele C. Weigle Department of Computer Science at Old Dominion University Vehicular Networks, Sensor Networks, and Internet Traffic Research http://oducs-networking.blogspot.com/ {marbabi, mweigle}@cs.odu.edu Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 28 This work was supported in part by the National Science Foundation under grant CNS-0721586.