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

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PhD Defense Presentation...

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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  • 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
  • 6. Motivation
    Real-time monitoring of traffic
    • TMCs need high quality data
    • 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 systems
    How 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
  • 19. 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 marbabi@cs.odu.edu
    16
    Note:
    • Using traffic in opposite direction
    • 20. Hybrid adds some redundancy
    • 21. Message Delay?
  • Message Delay
    Hadi Arbabi marbabi@cs.odu.edu
    17
    nf= total number of distinct received forwarded messages received by forwarding
    nc = total number of distinct received carried messages
    n = total number of distinct received messages
    tf = forwarding delay ≈ 0.0
    tc = carrying delay ≈ average travel time
    wf = nf/n
    wc = nc/n
  • 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
  • 23. Using Our Contributed Integrated VANET Simulator
    Hadi Arbabi marbabi@cs.odu.edu
    19
    Several experiments using VANET modules that we developed for the ns-3 simulator
    • 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. Highway Mobility for Vehicular Networks (Project and Google Code)
    • 25. http://code.google.com/p/ns-3-highway-mobility/
  • Free Flow Traffic (Eval.)
    Bi-directional six-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)
    Free flow traffic with poor connectivity
    Desired speed 110±18 km/h (30±5 m/s)
    Hadi Arbabi marbabi@cs.odu.edu
    20
  • 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