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A Framework for Dynamic Traffic Monitoring Using Vehicular Ad-Hoc Networks
 

A Framework for Dynamic Traffic Monitoring Using Vehicular Ad-Hoc Networks

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Hadi Arbabi's PhD Defense Slides ...

Hadi Arbabi's PhD Defense Slides
Department of Computer Science
Old Dominion University
April 21, 2011

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    A Framework for Dynamic Traffic Monitoring Using Vehicular Ad-Hoc Networks A Framework for Dynamic Traffic Monitoring Using Vehicular Ad-Hoc Networks Presentation Transcript

    • 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
    • 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
    • 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)
    • 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
    • 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
    • Motivation
      Real-time monitoring of traffic
      • TMCs need high quality data
      • Fixed point sensors and detectors cannot estimate travel time and space mean speed and they are not flexible
      • High demand for accurate estimation of travel time and speed
      • 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
    • 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
    • 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
    • Task Organizer and Virtual Strips
      Hadi Arbabi marbabi@cs.odu.edu
      9
      Virtual Strip
      TO
      Virtual Segment
      Medium
      Virtual Strip
      TMC
    • 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
    • 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
    • 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
    • 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
    • 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
    • What Message Delivery Method?
      Hadi Arbabi marbabi@cs.odu.edu
      15
      Flow Rate
      1800
      3600
      5400
      veh/h
      Transmission Range
    • 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
      • Hybrid adds some redundancy
      • 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
    • 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
    • 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
      • Highway Mobility for Vehicular Networks (Project and Google Code)
      • 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
    • 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
    • Freception
      Hadi Arbabi marbabi@cs.odu.edu
      22
      Higher Penetration = Higher RF
      Farther Distance = Lower RF
    • Message Reception Rate (MRR)
      Hadi Arbabi marbabi@cs.odu.edu
      23
      Hybrid = Forwarding + Carrying = Full MRR
      Higher Penetration = More Forwarding = Less Carrying
      VS2
      50%
    • MRR and Traffic In Opposite Direction
      Hadi Arbabi marbabi@cs.odu.edu
      24
      20-25%
      20-25%
    • 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
    • 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
    • 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
    • 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
    • Travel Time
      Hadi Arbabi marbabi@cs.odu.edu
      29
      Quality of Data
      RF+SAC >= RF > AVL
      VS2
      VS2
    • Space Mean Speed (SMS)
      Hadi Arbabi marbabi@cs.odu.edu
      30
      VS2
      VS2
    • Flow Rate
      Hadi Arbabi marbabi@cs.odu.edu
      31
      Count Information (e.g., Flow Rate and Volume)
      Only in High PR
      VS2
    • 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
    • Quality of Data
      Hadi Arbabi marbabi@cs.odu.edu
      33
      t-test Alpha = 0.05 (Confidence > 95%)
    • Quality of Data
      Hadi Arbabi marbabi@cs.odu.edu
      34
      t-test Alpha = 0.05 Confidence > 95%
    • 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
    • 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
    • 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
    • Time Mean Speed (TMS)
      Hadi Arbabi marbabi@cs.odu.edu
      38
    • Time/Space/Speed
      Hadi Arbabi marbabi@cs.odu.edu
      39
      VS4
      VS3.5
      VS3
      VS2.5
      VS2
    • 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?
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • Avg. visit 150/mon [code + paper]Avg. new user 10/mon [our simulator]in past 9 months!
      Hadi Arbabi marbabi@cs.odu.edu
      47
    • Expansion of its Academic Use
      Hadi Arbabi marbabi@cs.odu.edu
      48
    • 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
    • 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.
    • 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
    • Hadi Arbabi marbabi@cs.odu.edu
      52