Using DTMon to Monitor Transient Flow Traffic

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We evaluate the performance of the DTMon dynamic traffic monitoring system to measure travel times and speeds in transient flow traffic caused by non-recurring congestion. DTMon uses vehicular …

We evaluate the performance of the DTMon dynamic traffic monitoring system to measure travel times and speeds in transient flow traffic caused by non-recurring congestion. DTMon uses vehicular networks and roadside infrastructure to collect data from passing vehicles. We show DTMon's ability to gather high-quality real-time traffic data such as travel time and speed. These metrics can be used to detect transitions in traffic flow (e.g., caused by congestion) especially where accurate flow rate information is not available. We evaluate the accuracy and latency of DTMon in providing traffic measurements using two different methods of message delivery. We show the advantages of using dynamically-defined measurement points for monitoring transient flow traffic. We compare DTMon with currently in-use probe-based systems (e.g., AVL) and fixed-point sensors and detectors (e.g., ILD).

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  • 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
  • 2. Motivation
    Real-time monitoring of traffic
    • Accurate estimation of travel time and speed
    • 3. Required in transient flow traffic (e.g., congestion)
    • 4. Fixed point sensors and detectors cannot estimate travel time and space mean speed
    Trends 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
  • 14. Evaluation
    Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu
    12
    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
    • 15. Highway Mobility for Vehicular Networks (Project and Google Code)
    • 16. http://code.google.com/p/ns-3-highway-mobility/
  • (Overview)
    In our previous work
    Message Reception
    Effect of Traffic Density, Flow Rate, Speed
    Effect of Market Penetration Rate
    Effect of Transmission Range
    Effect of Traffic In Opposite Direction
    Distance From TOs
    Latency and Message Delay
    Comparison among Methods of Message Delivery
    Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu
    13
    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
  • 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.