This document describes a framework called DTMon for dynamic traffic monitoring using vehicular ad-hoc networks. DTMon uses task organizers and virtual strips to spatially sample traffic conditions from probe vehicles. It was evaluated using a network simulator and shown to accurately estimate travel time, speed and detect congestion using only forwarding messages. The contributions of DTMon include methods for dynamic traffic monitoring using VANETs, analysis of factors impacting data quality, and demonstrating its usefulness in monitoring congested traffic conditions.

1. Hadi Arbabi PhD in Computer Science Department Of Computer Science Old Dominion University Advisor: Dr. Michele C. Weigle M.S. in Computer Science Old Dominion University, May 2007 Advisor: Dr. Stephan Olariu B.S. in Computer Engineering Shiraz University , June 2001 A FRAMEWORK FOR DYNAMIC TRAFFIC Monitoring USING VEHICULAR AD-HOC NETWORKs

2. Content INTRODUCTION Traffic Monitoring and Technologies in Use Motivations and Our Approach DTMon: Dynamic Traffic Monitoring Components Deployment Investigation Analysis EVALUATION Free-Flow Traffic Transient Flow Traffic Traffic with Congestion CONCLUSION CONTRIBUTIONS Hadi Arbabi marbabi@cs.odu.edu 2

3. Introduction Traffic Monitoring Vehicle classification Count information Flow rate Volume Density Traffic speed Time mean speed (TMS) Space mean speed (SMS) Travel time (TT) Hadi Arbabi marbabi@cs.odu.edu 3 Traffic Management Center (TMC)

4. Monitoring Techniques Spatial Probing (Sensing) Fixed Point Sensors and Detectors Inductive loop detectors (ILDs) Acoustic sensors Microwave radar sensors Video cameras Hadi Arbabi marbabi@cs.odu.edu 4 Adv.:Speed (TMS), flow rate, volume, density Disadv.:Static, locations must be carefully chosen in advance, no travel times

5. Monitoring Techniques Temporal Probing Probe vehicle-based system Automatic vehicle location (AVL) Wireless location technology (WLT) Hadi Arbabi marbabi@cs.odu.edu 5 e.g., probing vehicles every 5, 10, 15, 30, or 60 seconds Adv.:Real-time monitoring, travel times, speed (SMS) Disadv.:Affected by market penetration rate, hard to extrapolate some stats, must interpolate to estimate stats at a particular location

7. Fixed point sensors and detectors cannot estimate travel time and space mean speed and they are not flexible

8. High demand for accurate estimation of travel time and speed

9. Trend toward probe vehicle-based systemsHow can vehicular ad-hoc networks (VANETs) be used? Requires investigations Augment current technologies? Hadi Arbabi marbabi@cs.odu.edu 6 Investigation

10. Related Work NOTICE (Abuelela et. al, IEEE (VTC), 2008) VANETs + Belts CarTel(Hull et. al, SenSys, 2006) Uses cell phones and cars as nodes in a dynamic sensor network TrafficView(Nadeem, IEEE (MDM), 2004) Scalable traffic monitoring system for inter-vehicle communication considering road conditions GEMS project (http://www.path.berkeley.edu) Based on AVL and WLT technologies Mobile Millennium project (http://traffic.berkeley.edu) Cell phones Nirecell(ACM SenSys 2008) Smart phones Traffic.com, Inrix, etc. Deployed microwave radar sensors and acoustic sensors in combination with data collected by DOT sensors Hadi Arbabi marbabi@cs.odu.edu 7

11. OUR APPROACHDynamic Traffic Monitoring (DTMon) DTMon - A probe vehicle-based system using VANET and dynamically defined points of interest on the road Task Organizers (TOs) Vehicles Virtual Strips (VS) Imaginary lines or points Hadi Arbabi marbabi@cs.odu.edu 8 *A dynamic spatial probing without disadvantages of temporal probing

12. Task Organizer and Virtual Strips Hadi Arbabi marbabi@cs.odu.edu 9 Virtual Strip TO Virtual Segment Medium Virtual Strip TMC

13. Task Organizer (TO) Communicates with passing vehicles Assigns measurement tasks Collects reports from the vehicles Organizes received measurements Informs upcoming traffic conditions Multiple TOs (also can be moveable) Centralized Aggregate information about the whole region Hadi Arbabi marbabi@cs.odu.edu 10

14. 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 (or Message) Forwarded to the listed TOs Stored and carried to the next available TO Hadi Arbabi marbabi@cs.odu.edu 11 Type: Volume-Speed-Travel-Time Delivery Method: Forwarding (RF) Source TO: TOA (xa,ya, za) Target TO: TOA(xa,ya, za) Target Strips: VS1(X1, Y1, Z1),VS2,VS3,... A Sample Task from A TO A Sample Header of A Message or A Report

15. Deployment Multiple VS and Segments Dynamically Defined Multiple TOs Hadi Arbabi marbabi@cs.odu.edu 12 Type: Volume-Speed Delivery Method: Store-and-Carry (SAC) Source TO: TOA (xa,ya, za) Target TO: TOB (xb,yb, zb) Target Strips: VS1,VS2,VS3,... A Sample Task From TO to Vehicles

16. Investigation Amount of Information Delivered to TO Market Penetration Rate (PR) Message Reception Rate (MRR) Information Reception Rate (IRR) IRR ≈ MRR x PR Various Traffic Characteristics Traffic conditions (speed, flow, density) Inter-Vehicle Spacing Distance to TO Transmission Range Message Delay (and Latency) Quality of Traffic data Delivery Methods, Type of Data, etc. Hadi Arbabi marbabi@cs.odu.edu 13 MRR for a VS = #MSG Recv. / #MSG Generated IRR for a VS = #MSG Recv. / #Vehicles Passed

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

18. What Message Delivery Method? Hadi Arbabi marbabi@cs.odu.edu 15 Flow Rate 1800 3600 5400 veh/h Transmission Range

20. Hybrid adds some redundancy

22. Performance Evaluation of DTMon Traffic Conditions Free Flow Traffic Transient Flow Traffic Transient Congestion Extended Congestion Compare Delivery Methods Message Reception Rate Message Delay and Latency Quality of Data (estimated measurements) Compare with Probe Vehicle-Based Systems (e.g., AVL) Fixed Point Sensors and Detectors (e.g., ILD) Hadi Arbabi marbabi@cs.odu.edu 18 Methods that can collect more informationfrom vehicles with less latencyare preferred in up-to-date traffic monitoring

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

26. Free Flow Traffic (Eval.) 10 runs, 30 min each, PR {5%, 25%, 50%, 100%} Major defined strips by TOs {VS1 , VS2 , VS5 , VS9} Compute avg., variance, significance, etc. Comparison Each delivery method with the others Actual simulation (ground truth) data Hadi Arbabi marbabi@cs.odu.edu 21

27. Freception Hadi Arbabi marbabi@cs.odu.edu 22 Higher Penetration = Higher RF Farther Distance = Lower RF

28. Message Reception Rate (MRR) Hadi Arbabi marbabi@cs.odu.edu 23 Hybrid = Forwarding + Carrying = Full MRR Higher Penetration = More Forwarding = Less Carrying VS2 50%

29. MRR and Traffic In Opposite Direction Hadi Arbabi marbabi@cs.odu.edu 24 20-25% 20-25%

30. Message Delay Hadi Arbabi marbabi@cs.odu.edu 25 RF Delay Very Low Hybrid Delay 1. Amount of Carried Messages 2. TT More Forwarding Less Delay More SAC More Delay

31. Transient Flow Traffic (Eval.) 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) Desired speed 65±5 mph (29±2.2 m/s) Normal Distribution 20% of vehicles are Truck (for comparison with AVL) Uniform Distribution Hadi Arbabi marbabi@cs.odu.edu 26 A vehicle breaks down for 5 min

32. Transient Flow Traffic (Eval.) 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% Compute avg., variance, significance, etc. Hadi Arbabi marbabi@cs.odu.edu 27

33. Estimated Travel Time (ILDs vs. Actual) Hadi Arbabi marbabi@cs.odu.edu 28 Fixed Point Sensor and Detector’s Poor Estimation of TT and SMS

34. Travel Time Hadi Arbabi marbabi@cs.odu.edu 29 Quality of Data RF+SAC >= RF > AVL VS2 VS2

35. Space Mean Speed (SMS) Hadi Arbabi marbabi@cs.odu.edu 30 VS2 VS2

36. Flow Rate Hadi Arbabi marbabi@cs.odu.edu 31 Count Information (e.g., Flow Rate and Volume) Only in High PR VS2

37. Message Delay Hadi Arbabi marbabi@cs.odu.edu 32 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

38. Quality of Data Hadi Arbabi marbabi@cs.odu.edu 33 t-test Alpha = 0.05 (Confidence > 95%)

39. Quality of Data Hadi Arbabi marbabi@cs.odu.edu 34 t-test Alpha = 0.05 Confidence > 95%

40. Free Flow and Transient Flow (Summary) DTMoncan estimate good quality Travel Timeand 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 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 marbabi@cs.odu.edu 35

41. Traffic With Congestion (Eval.) Goal Use our findings about DTMon in detecting transitions in traffic flow using travel time and speed Show advantage of DTMon’s dynamically defined virtual strips by TOs For example, show DTMon’s ability in detecting congestion and the end of the queue No delay when RF is used Hadi Arbabi marbabi@cs.odu.edu 36

42. Example: End-of-Queue Detection During Congestion Using DTMon Create congestion near by VS4 (long period 30 min) Let TO1 dynamically define two additional new VS (VS2.5 and VS3.5 ) after the vehicle breaks down Observe transitions in travel times and speeds for each virtual strip, segments, and new sub-segments Hadi Arbabi marbabi@cs.odu.edu 37

43. Time Mean Speed (TMS) Hadi Arbabi marbabi@cs.odu.edu 38

44. Time/Space/Speed Hadi Arbabi marbabi@cs.odu.edu 39 VS4 VS3.5 VS3 VS2.5 VS2

45. Travel Time Hadi Arbabi marbabi@cs.odu.edu 40 VS3 VS2.5 VS2 Congestion Must Have Reached VS2VS3 Upper Section Or Lower Section? VS2.5VS3 Or V2V2.5?

46. Congestion (Summary) Benefits of Dynamically Defined Virtual Strips in DTMon Spatial probing from traffic Ability to monitor various points with only one TO Ability to monitor various segments with only one TO Ability to create virtual sub-segments No need for extrapolation/interpolation Detection of the end of the queue No flow rate information is required Speeds and travel times are sufficient No delay (using RF) Hadi Arbabi marbabi@cs.odu.edu 41

47. Contributions A method for using probe vehicles to perform spatial sampling of traffic conditions To provide real-time measurements of speed and travel time To allow for the measurements to be made at specific and dynamic locations of interest on the roadway To avoid the need for interpolation and estimation that is required when temporal sampling of probe vehicles is performed Hadi Arbabi marbabi@cs.odu.edu 42

48. Contributions An analysis of the factors that can impact the quality of monitored traffic data when using vehicular networks Market penetration rate Traffic conditions Communication range Distance between communicating entities Methods of message delivery Information and message reception rate Message delay Hadi Arbabi marbabi@cs.odu.edu 43

49. Contributions An evaluation of the impact of different methods of message delivery on the quality of traffic data that can be gathered by vehicular networks Regular forwarding Dynamic transmission range Store-and-carry Hybrid Comparisons Information and message reception rates Message delay (and latency) In-use technologies Hadi Arbabi marbabi@cs.odu.edu 44

50. Contributions A demonstration of the usefulness of DTMon’s monitoring approach for monitoring congested traffic conditions To allow a TMC to dynamically place additional monitoring points (virtual strips) in locations where congestion is building up To detect transitions in traffic flow using travel times and speeds, without having to rely on flow rate information To detect and track the end-of-the-queue in traffic with congestion Hadi Arbabi marbabi@cs.odu.edu 45

51. Contributions Highway mobility modules for the ns-3 network simulator The first highway mobility modules designed to produce realistic vehicle mobility and communications in ns-3 Validated modules have been released to the ns-3 community and are now being used by other researchers around the world Hadi Arbabi marbabi@cs.odu.edu 46

52. Avg. visit 150/mon [code + paper]Avg. new user 10/mon [our simulator]in past 9 months! Hadi Arbabi marbabi@cs.odu.edu 47

53. Expansion of its Academic Use Hadi Arbabi marbabi@cs.odu.edu 48

54. Future Work Investigate the usage of the most recent security/routing techniques and algorithms in VANETs suitable for DTMon Adapt DTMon and the same framework toward mobile nodes (e.g., cell phones) TOs are service providers (or TMCs) and … Vehicles are smart-phones (and with installed DTMon apps) Apps are updated with most recent defined virtual strips for the region Extend our implementation of VANET simulation modules for urban areas (e.g., intersections) Add the ability to read in and use detailed maps instead of a single straight highway Investigate the use of dynamically-defined virtual strips and TOs in DTMon to evaluate the performance of our proposed framework in urban area Methods to estimate the market penetration rate Hadi Arbabi marbabi@cs.odu.edu 49

55. Questions? Hadi Arbabi Department of Computer Science at Old Dominion University Vehicular Networks, Sensor Networks, and Internet Traffic Research http://oducs-networking.blogspot.com/ Source Code Wiki: Installation and Documentation http://code.google.com/p/ns-3-highway-mobility/ marbabi@cs.odu.edu Hadi Arbabi marbabi@cs.odu.edu 50 This work was supported in part by the National Science Foundation under grants CNS-0721586 and CNS-0709058.

56. Publications 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. Hadi Arbabi and Michele C. Weigle, “Using DTMon to Monitor Transient Flow Traffic”, In Proceedings of the IEEE Vehicular NetworkingConference (VNC). Jersey City, NJ, December 2010. Hadi Arbabi and Michele C. Weigle, “Highway Mobility and Vehicular Ad-Hoc Networks in ns-3,” In Proceedings of the Winter Simulation Conference. Baltimore, MD, December 2010. Hadi Arbabi and Michele C. Weigle, "Using Vehicular Networks to Collect Common Traffic Data," In Proceedings of the ACM International Workshop on Vehicular Internetworking (VANET). Beijing, September 2009. Hadi Arbabi, "Channel Management in Heterogeneous Cellular Networks", Master's Thesis, June 2007. Hadi Arbabi, "PCI Interface to Control Parallel Stepper Motors Simultaneously: Design, Implementation, Driver, and GUI", Bachelor's Thesis and Technical Report, June 2001. Hadi Arbabi marbabi@cs.odu.edu 51

57. Hadi Arbabi marbabi@cs.odu.edu 52