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Estimating density and lane inflow on a freeway segment

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  • Coifman, Benjamin

Abstract

This paper illustrates a methodology for estimating density in a freeway lane between detector stations and measuring the net number of vehicles to enter (or leave) the lane, i.e., the lane inflow. The work uses vehicle arrivals at each detector station and information from a sparse vehicle reidentification system, one that may match the measurements from as few as 5% of the vehicles that pass both detector stations. Such reidentification systems have already been documented and deployed for dual loop detectors and they will be used for illustration. Of course this work is also applicable to other vehicle reidentification systems, which may be based on other detection technologies, that may have a higher rate of reidentification.

Suggested Citation

  • Coifman, Benjamin, 2003. "Estimating density and lane inflow on a freeway segment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(8), pages 689-701, October.
    • Handle: RePEc:eee:transa:v:37:y:2003:i:8:p:689-701
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    References listed on IDEAS

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    1. Coifman, Benjamin & Cassidy, Michael, 2002. "Vehicle reidentification and travel time measurement on congested freeways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(10), pages 899-917, December.
    2. Coifman, Benjamin, 2002. "Estimating travel times and vehicle trajectories on freeways using dual loop detectors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(4), pages 351-364, May.
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    6. Man-Feng Chang & Dunos C. Gazis, 1975. "Traffic Density Estimation with Consideration of Lane-Changing," Transportation Science, INFORMS, vol. 9(4), pages 308-320, November.
    7. Sheu, Jiuh-Biing, 1999. "A stochastic modeling approach to dynamic prediction of section-wide inter-lane and intra-lane traffic variables using point detector data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(2), pages 79-100, February.
    8. Coifman, Benjamin, 2003. "Identifying the onset of congestion rapidly with existing traffic detectors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(3), pages 277-291, March.
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    Cited by:

    1. Coifman, Benjamin A. & Mallika, Ramachandran, 2007. "Distributed surveillance on freeways emphasizing incident detection and verification," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(8), pages 750-767, October.
    2. Li, Xiang & Sun, Jian-Qiao, 2017. "Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 41-58.
    3. Bekiaris-Liberis, Nikolaos & Roncoli, Claudio & Papageorgiou, Markos, 2017. "Highway traffic state estimation per lane in the presence of connected vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 1-28.
    4. Coifman, Benjamin, 2015. "Empirical flow-density and speed-spacing relationships: Evidence of vehicle length dependency," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 54-65.
    5. Coifman, Benjamin & Ponnu, Balaji & El Asmar, Paul, 2023. "LWR and shockwave analysis - Failures under a concave fundamental diagram and unexpected induced disturbances," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    6. Jin, Wen-Long, 2010. "A kinematic wave theory of lane-changing traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 1001-1021, September.
    7. Qiu, Tony Z. & Lu, Xiao-Yun & Chow, Andy H. F. & Shladover, Steven, 2009. "Real-time Density Estimation on Freeway with Loop Detector and Probe Data," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1pv3m9f4, Institute of Transportation Studies, UC Berkeley.
    8. Blake Davis & Ang Ji & Bichen Liu & David Levinson, 2020. "Moving Array Traffic Probes," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    9. Xu, Chengcheng & Liu, Pan & Wang, Wei & Li, Zhibin, 2014. "Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 58-70.
    10. Bakibillah, A.S.M. & Tan, Yong Hwa & Loo, Junn Yong & Tan, Chee Pin & Kamal, M.A.S. & Pu, Ziyuan, 2022. "Robust estimation of traffic density with missing data using an adaptive-R extended Kalman filter," Applied Mathematics and Computation, Elsevier, vol. 421(C).

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