We present DTMon, a dynamic traffic monitiroing system using vehicular networks, and analyze its performance in free flow (i.e., non-congested) traffic. DTMon uses roadside infrastructure to gather and report current traffic conditions to traffic management centers and equipped vehicles. We analyze how traffic characteristics such as speed, flow rate, percentage of communicating vehicles, and distance from the DTMon measurement point to the roadside infrastructure affects the amount and quality of data that can be gathered and delivered. We evaluate five different methods of delivering data from vehicles to the roadside infrastructure, including pure vehicle-to-vehicle communication, store-and-carry, and hybrid methods. Methods that employ some amount of store-and-carry can increase the delivery rate, but also increase the message delay. We show that with just a few pieces of roadside infrastructure, DTMon can gather high-quality travel time and speed data even with a low percentage of communicating vehicles.

1. Hadi Arbabi and Michele C. Weigle Department Of Computer Science Old Dominion University Monitoring FREE FLOW TRAFFIC USING VEHICULAR NETWORKs 3rd IEEE Intelligent Vehicular Communications System Workshop (IVCS), January 2001, Las Vegas

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 systems Dynamic points of interest Augment current technologies Effect of Market Penetration Rate 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 APPROACH Monitoring Traffic Data in Rural Areas Highways Message Reception Methods of Message Delivery EVALUATION Free-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 TOsMultiple 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. Message Reception (Analysis) Amount of Information Delivered to TO Message Reception Rate (MRR) Information Reception Rate (IRR) Analyze Various Traffic Characteristics Traffic Speed, Density, Flow Rate Inter-Vehicle Spacing Equipped Vehicles Market Penetration Rate (PR) Distance to TO Transmission Range Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 11

14. Message Reception Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 12 B = inter-vehicle spacing p = penetration rate S = mean speed v = flow rate E = inter-vehicle spacing of equipped vehicles R0 = transmission range d = distance to TO E[C] = expected inter-vehicle spacing

15. What Message Delivery Method? Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 13 Flow Rate 1800 3600 5400 veh/h Transmission Range

16. Methods of Message Delivery Regular Forwarding (RF) Dynamic Transmission Range (DTR) Store-and-Carry (SAC) If Multiple TOs Hybrid RF+SAC DTR+SAC Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 14

17. Evaluation Compare Delivery Methods Penetration Rate (PR) Message Reception Rate (MRR) Information Reception Rate (IRR) IRR ≈ MRR x PR Message Delay Quality of Traffic data Delivery Methods and Type of Data Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 15

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

21. Simulation Setup 10 runs, 30 min each, p {5%, 25%, 50%, 100%} Major defined strips by TOs {VS1 , VS2 , VS5 , VS9} Comparison Each method with the others Actual simulation (ground truth) data Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 18

22. Freception Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 19 Higher Penetration = Higher RF Farther Distance= Lower RF

23. MRR Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 20 Hybrid = Forwarding + Carrying = Full MRR Higher Penetration = More Forwarding = Less Carrying VS2 50%

24. MRR Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 21 Higher Distance Does Not Affect SAC or Hybrid Higher Distance = No Forwarding Lower Distance = Higher Forwarding VS2 50%

25. MRR and Traffic In Opposite Direction Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 22 20-25% 20-25%

26. Message Delay Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 23 RF Delay Very Low Hybrid Delay 1. Amount of Carried Messages 2. TT More Forwarding Less Delay More SAC More Delay

27. Quality of Data Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 24 t-test Alpha = 0.05 (Confidence > 95%)

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

29. Summary DTMoncan estimate good quality Travel Time and Speed DTMoncan estimate good quality flow rate and density in higher penetration rates Hybrid message delivery improves information reception rate with cost of latency as an option for low penetration rates DTMoncan augment current technologies and monitoring systems Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 26

30. 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 27 This work was supported in part by the National Science Foundation under grant CNS-0721586.