A Framework for Dynamic Traffic Monitoring using Vehicular Ad-hoc Networks


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PhD Defense Presentation

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

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A Framework for Dynamic Traffic Monitoring using Vehicular Ad-hoc Networks

  1. 1. Hadi Arbabi<br />PhD in Computer Science<br />Department Of Computer Science<br />Old Dominion University<br />Advisor: Dr. Michele C. Weigle<br />M.S. in Computer Science<br />Old Dominion University, May 2007 Advisor: Dr. Stephan Olariu<br />B.S. in Computer Engineering <br />Shiraz University , June 2001<br />A FRAMEWORK FOR DYNAMIC TRAFFIC Monitoring USING VEHICULAR AD-HOC NETWORKs<br />
  2. 2. Content<br />INTRODUCTION<br />Traffic Monitoring and Technologies in Use<br />Motivations and Our Approach<br />DTMon: Dynamic Traffic Monitoring<br />Components<br />Deployment<br />Investigation<br />Analysis<br />EVALUATION<br />Free-Flow Traffic<br />Transient Flow Traffic<br />Traffic with Congestion<br />CONCLUSION<br />CONTRIBUTIONS<br />Hadi Arbabi marbabi@cs.odu.edu<br />2<br />
  3. 3. Introduction<br />Traffic 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 marbabi@cs.odu.edu<br />3<br />Traffic Management Center (TMC)<br />
  4. 4. Monitoring Techniques<br />Spatial Probing (Sensing)<br />Fixed Point Sensors and Detectors<br />Inductive loop detectors (ILDs)<br />Acoustic sensors<br />Microwave radar sensors<br />Video cameras<br />Hadi Arbabi marbabi@cs.odu.edu<br />4<br />Adv.:Speed (TMS), flow rate, volume, density<br />Disadv.:Static, locations must be carefully chosen in advance, no travel times <br />
  5. 5. Monitoring Techniques<br />Temporal Probing<br />Probe vehicle-based system<br />Automatic vehicle location (AVL)<br />Wireless location technology (WLT)<br />Hadi Arbabi marbabi@cs.odu.edu<br />5<br />e.g., probing vehicles every <br />5, 10, 15, 30, or 60 seconds<br />Adv.:Real-time monitoring, travel times, speed (SMS)<br />Disadv.:Affected by market penetration rate,<br />hard to extrapolate <br />some stats, <br />must interpolate <br />to estimate stats at <br />a particular location <br />
  6. 6. Motivation<br />Real-time monitoring of traffic <br /><ul><li>TMCs need high quality data
  7. 7. Fixed point sensors and detectors cannot estimate travel time and space mean speed and they are not flexible
  8. 8. High demand for accurate estimation of travel time and speed
  9. 9. Trend toward probe vehicle-based systems</li></ul>How can vehicular ad-hoc networks (VANETs) be used?<br />Requires investigations<br />Augment current technologies?<br />Hadi Arbabi marbabi@cs.odu.edu<br />6<br />Investigation<br />
  10. 10. Related Work<br />NOTICE (Abuelela et. al, IEEE (VTC), 2008)<br />VANETs + Belts<br />CarTel(Hull et. al, SenSys, 2006)<br />Uses cell phones and cars as nodes in a dynamic sensor network<br />TrafficView(Nadeem, IEEE (MDM), 2004)<br />Scalable traffic monitoring system for inter-vehicle communication considering road conditions<br />GEMS project (http://www.path.berkeley.edu)<br />Based on AVL and WLT technologies <br />Mobile Millennium project (http://traffic.berkeley.edu)<br />Cell phones<br />Nirecell(ACM SenSys 2008)<br />Smart phones<br />Traffic.com, Inrix, etc.<br />Deployed microwave radar sensors and acoustic sensors in combination with data collected by DOT sensors<br />Hadi Arbabi marbabi@cs.odu.edu<br />7<br />
  11. 11. OUR APPROACHDynamic Traffic Monitoring (DTMon)<br />DTMon - A probe vehicle-based system using VANET and dynamically defined points of interest on the road <br />Task Organizers (TOs)<br />Vehicles<br />Virtual Strips (VS)<br />Imaginary lines or points<br />Hadi Arbabi marbabi@cs.odu.edu<br />8<br />*A dynamic spatial probing <br />without disadvantages of <br />temporal probing<br />
  12. 12. Task Organizer and Virtual Strips<br />Hadi Arbabi marbabi@cs.odu.edu<br />9<br />Virtual Strip<br />TO<br />Virtual Segment<br />Medium<br />Virtual Strip<br />TMC<br />
  13. 13. 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 (also can be moveable)<br />Centralized<br />Aggregate information about the whole region<br />Hadi Arbabi marbabi@cs.odu.edu<br />10<br />
  14. 14. 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 (or Message)<br />Forwarded to the listed TOs<br />Stored and carried to the next available TO<br />Hadi Arbabi marbabi@cs.odu.edu<br />11<br />Type: Volume-Speed-Travel-Time<br />Delivery Method: Forwarding (RF)<br />Source TO: TOA (xa,ya, za)<br />Target TO: TOA(xa,ya, za)<br />Target Strips: VS1(X1, Y1, Z1),VS2,VS3,...<br />A Sample Task from A TO<br />A Sample Header of A Message or A Report<br />
  15. 15. Deployment <br />Multiple VS and Segments<br />Dynamically Defined<br />Multiple TOs<br />Hadi Arbabi marbabi@cs.odu.edu<br />12<br />Type: Volume-Speed<br />Delivery Method: Store-and-Carry (SAC)<br />Source TO: TOA (xa,ya, za)<br />Target TO: TOB (xb,yb, zb)<br />Target Strips: VS1,VS2,VS3,...<br />A Sample Task From TO to Vehicles<br />
  16. 16. Investigation<br />Amount of Information Delivered to TO<br />Market Penetration Rate (PR)<br />Message Reception Rate (MRR)<br />Information Reception Rate (IRR)<br />IRR ≈ MRR x PR<br />Various Traffic Characteristics <br />Traffic conditions (speed, flow, density)<br />Inter-Vehicle Spacing<br />Distance to TO<br />Transmission Range<br />Message Delay (and Latency)<br />Quality of Traffic data<br />Delivery Methods, Type of Data, etc.<br />Hadi Arbabi marbabi@cs.odu.edu<br />13<br />MRR for a VS = #MSG Recv. / #MSG Generated <br />IRR for a VS = #MSG Recv. / #Vehicles Passed <br />
  17. 17. Message Reception<br />Hadi Arbabi marbabi@cs.odu.edu<br />14<br />B = inter-vehicle spacing<br />p = penetration rate<br />S = mean speed<br />v = flow rate<br />Ep = inter-vehicle spacing of equipped vehicles<br />R0 = transmission range <br />d = distance to TO<br />E[C] = expected inter-vehicle spacing<br />
  18. 18. What Message Delivery Method?<br />Hadi Arbabi marbabi@cs.odu.edu<br />15<br />Flow Rate<br />1800<br />3600<br />5400<br />veh/h<br />Transmission Range<br />
  19. 19. Methods of Message Delivery<br />Regular Forwarding (RF)<br />Dynamic Transmission Range (DTR)<br />Store-and-Carry (SAC)<br />If Multiple TOs<br />Hybrid<br />RF+SAC<br />DTR+SAC<br />Hadi Arbabi marbabi@cs.odu.edu<br />16<br />Note: <br /><ul><li> Using traffic in opposite direction
  20. 20. Hybrid adds some redundancy
  21. 21. Message Delay?</li></li></ul><li>Message Delay<br />Hadi Arbabi marbabi@cs.odu.edu<br />17<br />nf= total number of distinct received forwarded messages received by forwarding<br />nc = total number of distinct received carried messages<br />n = total number of distinct received messages<br />tf = forwarding delay ≈ 0.0<br />tc = carrying delay ≈ average travel time<br />wf = nf/n<br />wc = nc/n<br />
  22. 22. Performance Evaluation of DTMon<br />Traffic Conditions<br />Free Flow Traffic<br />Transient Flow Traffic<br />Transient Congestion<br />Extended Congestion<br />Compare Delivery Methods<br />Message Reception Rate<br />Message Delay and Latency<br />Quality of Data (estimated measurements)<br />Compare with<br />Probe Vehicle-Based Systems (e.g., AVL)<br />Fixed Point Sensors and Detectors (e.g., ILD)<br />Hadi Arbabi marbabi@cs.odu.edu<br />18<br />Methods that can collect more informationfrom vehicles with less latencyare preferred in up-to-date traffic monitoring<br />
  23. 23. Using Our Contributed Integrated VANET Simulator<br />Hadi Arbabi marbabi@cs.odu.edu<br />19<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
  24. 24. Highway Mobility for Vehicular Networks (Project and Google Code)
  25. 25. http://code.google.com/p/ns-3-highway-mobility/</li></li></ul><li>Free Flow Traffic (Eval.)<br />Bi-directional six-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 />Free flow traffic with poor connectivity<br />Desired speed 110±18 km/h (30±5 m/s)<br />Hadi Arbabi marbabi@cs.odu.edu<br />20<br />
  26. 26. Free Flow Traffic (Eval.)<br />10 runs, 30 min each, PR {5%, 25%, 50%, 100%}<br />Major defined strips by TOs {VS1 , VS2 , VS5 , VS9}<br />Compute avg., variance, significance, etc.<br />Comparison<br />Each delivery method with the others<br />Actual simulation (ground truth) data<br />Hadi Arbabi marbabi@cs.odu.edu<br />21<br />
  27. 27. Freception<br />Hadi Arbabi marbabi@cs.odu.edu<br />22<br />Higher Penetration = Higher RF<br />Farther Distance = Lower RF<br />
  28. 28. Message Reception Rate (MRR)<br />Hadi Arbabi marbabi@cs.odu.edu<br />23<br />Hybrid = Forwarding + Carrying = Full MRR<br />Higher Penetration = More Forwarding = Less Carrying<br />VS2<br />50%<br />
  29. 29. MRR and Traffic In Opposite Direction<br />Hadi Arbabi marbabi@cs.odu.edu<br />24<br />20-25% <br />20-25% <br />
  30. 30. Message Delay<br />Hadi Arbabi marbabi@cs.odu.edu<br />25<br />RF Delay Very Low<br />Hybrid Delay <br />1. Amount of Carried Messages<br />2. TT<br />More Forwarding<br />Less Delay<br />More SAC <br />More Delay<br />
  31. 31. Transient Flow Traffic (Eval.)<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 />Desired speed 65±5 mph (29±2.2 m/s)<br />Normal Distribution<br />20% of vehicles are Truck (for comparison with AVL) <br />Uniform Distribution<br />Hadi Arbabi marbabi@cs.odu.edu<br />26<br />A vehicle breaks down for 5 min<br />
  32. 32. Transient Flow Traffic (Eval.)<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 />Compute avg., variance, significance, etc.<br />Hadi Arbabi marbabi@cs.odu.edu<br />27<br />
  33. 33. Estimated Travel Time (ILDs vs. Actual)<br />Hadi Arbabi marbabi@cs.odu.edu<br />28<br />Fixed Point Sensor and Detector’s Poor Estimation of TT and SMS<br />
  34. 34. Travel Time<br />Hadi Arbabi marbabi@cs.odu.edu<br />29<br />Quality of Data<br />RF+SAC >= RF > AVL<br />VS2<br />VS2<br />
  35. 35. Space Mean Speed (SMS)<br />Hadi Arbabi marbabi@cs.odu.edu<br />30<br />VS2<br />VS2<br />
  36. 36. Flow Rate<br />Hadi Arbabi marbabi@cs.odu.edu<br />31<br />Count Information (e.g., Flow Rate and Volume)<br />Only in High PR<br />VS2<br />
  37. 37. Message Delay<br />Hadi Arbabi marbabi@cs.odu.edu<br />32<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 />
  38. 38. Quality of Data<br />Hadi Arbabi marbabi@cs.odu.edu<br />33<br />t-test Alpha = 0.05 (Confidence > 95%)<br />
  39. 39. Quality of Data<br />Hadi Arbabi marbabi@cs.odu.edu<br />34<br />t-test Alpha = 0.05 Confidence > 95%<br />
  40. 40. Free Flow and Transient Flow (Summary)<br />DTMoncan estimate good quality Travel Timeand 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 />Hybrid message delivery improves information reception rate with cost of latency as an option for low penetration rates<br />DTMoncan augment current technologies and monitoring systems<br />Hadi Arbabi marbabi@cs.odu.edu<br />35<br />
  41. 41. Traffic With Congestion (Eval.)<br />Goal<br />Use our findings about DTMon in detecting transitions in traffic flow using travel time and speed<br />Show advantage of DTMon’s dynamically defined virtual strips by TOs<br />For example, show DTMon’s ability in detecting congestion and the end of the queue<br />No delay when RF is used<br />Hadi Arbabi marbabi@cs.odu.edu<br />36<br />
  42. 42. Example: End-of-Queue Detection During Congestion Using DTMon<br />Create congestion near by VS4 (long period 30 min)<br />Let TO1 dynamically define two additional new VS (VS2.5 and VS3.5 ) after the vehicle breaks down<br />Observe transitions in travel times and speeds for each virtual strip, segments, and new sub-segments<br />Hadi Arbabi marbabi@cs.odu.edu<br />37<br />
  43. 43. Time Mean Speed (TMS)<br />Hadi Arbabi marbabi@cs.odu.edu<br />38<br />
  44. 44. Time/Space/Speed<br />Hadi Arbabi marbabi@cs.odu.edu<br />39<br />VS4<br />VS3.5<br />VS3<br />VS2.5<br />VS2<br />
  45. 45. Travel Time<br />Hadi Arbabi marbabi@cs.odu.edu<br />40<br />VS3<br />VS2.5<br />VS2<br />Congestion Must Have Reached VS2VS3<br />Upper Section Or Lower Section?<br />VS2.5VS3 Or V2V2.5?<br />
  46. 46. Congestion (Summary)<br />Benefits of Dynamically Defined Virtual Strips in DTMon<br />Spatial probing from traffic<br />Ability to monitor various points with only one TO<br />Ability to monitor various segments with only one TO<br />Ability to create virtual sub-segments<br />No need for extrapolation/interpolation<br />Detection of the end of the queue<br />No flow rate information is required<br />Speeds and travel times are sufficient <br />No delay (using RF)<br />Hadi Arbabi marbabi@cs.odu.edu<br />41<br />
  47. 47. Contributions<br />A method for using probe vehicles to perform spatial sampling of traffic conditions <br />To provide real-time measurements of speed and travel time<br />To allow for the measurements to be made at specific and dynamic locations of interest on the roadway<br />To avoid the need for interpolation and estimation that is required when temporal sampling of probe vehicles is performed<br />Hadi Arbabi marbabi@cs.odu.edu<br />42<br />
  48. 48. Contributions<br />An analysis of the factors that can impact the quality of monitored traffic data when using vehicular networks <br />Market penetration rate<br />Traffic conditions<br />Communication range<br />Distance between communicating entities<br />Methods of message delivery<br />Information and message reception rate<br />Message delay<br />Hadi Arbabi marbabi@cs.odu.edu<br />43<br />
  49. 49. Contributions<br />An evaluation of the impact of different methods of message delivery on the quality of traffic data that can be gathered by vehicular networks<br />Regular forwarding<br />Dynamic transmission range<br />Store-and-carry<br />Hybrid<br />Comparisons<br />Information and message reception rates<br />Message delay (and latency)<br />In-use technologies<br />Hadi Arbabi marbabi@cs.odu.edu<br />44<br />
  50. 50. Contributions<br />A demonstration of the usefulness of DTMon’s monitoring approach for monitoring congested traffic conditions<br />To allow a TMC to dynamically place additional monitoring points (virtual strips) in locations where congestion is building up<br />To detect transitions in traffic flow using travel times and speeds, without having to rely on flow rate information<br />To detect and track the end-of-the-queue in traffic with congestion<br />Hadi Arbabi marbabi@cs.odu.edu<br />45<br />
  51. 51. Contributions<br />Highway mobility modules for the ns-3 network simulator <br />The first highway mobility modules designed to produce realistic vehicle mobility and communications in ns-3 <br />Validated modules have been released to the ns-3 community and are now being used by other researchers around the world<br />Hadi Arbabi marbabi@cs.odu.edu<br />46<br />
  52. 52. Avg. visit 150/mon [code + paper]Avg. new user 10/mon [our simulator]in past 9 months!<br />Hadi Arbabi marbabi@cs.odu.edu<br />47<br />
  53. 53. Expansion of its Academic Use<br />Hadi Arbabi marbabi@cs.odu.edu<br />48<br />
  54. 54. Future Work<br />Investigate the usage of the most recent security/routing techniques and algorithms in VANETs suitable for DTMon<br />Adapt DTMon and the same framework toward mobile nodes (e.g., cell phones)<br />TOs are service providers (or TMCs) and …<br />Vehicles are smart-phones (and with installed DTMon apps)<br />Apps are updated with most recent defined virtual strips for the region<br />Extend our implementation of VANET simulation modules for urban areas (e.g., intersections) <br />Add the ability to read in and use detailed maps instead of a single straight highway<br />Investigate the use of dynamically-defined virtual strips and TOs in DTMon to evaluate the performance of our proposed framework in urban area <br />Methods to estimate the market penetration rate <br />Hadi Arbabi marbabi@cs.odu.edu<br />49<br />
  55. 55. Questions?<br />Hadi Arbabi<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 />Source Code<br />Wiki: Installation and Documentation<br />http://code.google.com/p/ns-3-highway-mobility/<br />marbabi@cs.odu.edu<br />Hadi Arbabi marbabi@cs.odu.edu<br />50<br />This work was supported in part by the National Science Foundation under grants CNS-0721586 and CNS-0709058.<br />
  56. 56. Publications<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 />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.<br />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.<br />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.<br />Hadi Arbabi, "Channel Management in Heterogeneous Cellular Networks", Master's Thesis, June 2007.<br />Hadi Arbabi, "PCI Interface to Control Parallel Stepper Motors Simultaneously: Design, Implementation, Driver, and GUI", Bachelor's Thesis and Technical Report, June 2001.<br />Hadi Arbabi marbabi@cs.odu.edu<br />51<br />
  57. 57. Hadi Arbabi marbabi@cs.odu.edu<br />52<br />