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Application of Kalman Filtering to the Surveillance and Control of Traffic Systems

Author

Listed:
  • Michael W. Szeto

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

  • Denos C. Gazis

    (IBM Watson Research Center, Yorktown Heights, New York)

Abstract

The methodology of the discrete-time, extended Kalman filter is applied for the estimation of densities and the control of critical traffic links. The methodology is tested using traffic data obtained at the Lincoln tunnel of New York City. Two algorithms are tested, one involving density estimation alone and one combining density estimation with a formalism for the determination of optimal control. The results indicate that the first algorithm gives very good density estimates. The second algorithm yields a less accurate density estimate, but has the advantage over the first that it is amenable to an analytical optimization investigation.

Suggested Citation

  • Michael W. Szeto & Denos C. Gazis, 1972. "Application of Kalman Filtering to the Surveillance and Control of Traffic Systems," Transportation Science, INFORMS, vol. 6(4), pages 419-439, November.
    • Handle: RePEc:inm:ortrsc:v:6:y:1972:i:4:p:419-439
    DOI: 10.1287/trsc.6.4.419
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    Cited by:

    1. Zheng, Fangfang & Jabari, Saif Eddin & Liu, Henry X. & Lin, DianChao, 2018. "Traffic state estimation using stochastic Lagrangian dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 143-165.
    2. Ngoduy, D., 2008. "Applicable filtering framework for online multiclass freeway network estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 599-616.
    3. Sheu, Jiuh-Biing & Chou, Yi-Hwa & Shen, Liang-Jen, 2001. "A stochastic estimation approach to real-time prediction of incident effects on freeway traffic congestion," Transportation Research Part B: Methodological, Elsevier, vol. 35(6), pages 575-592, July.
    4. Yuan, Yun & Zhang, Zhao & Yang, Xianfeng Terry & Zhe, Shandian, 2021. "Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 88-110.
    5. Jabari, Saif Eddin & Liu, Henry X., 2013. "A stochastic model of traffic flow: Gaussian approximation and estimation," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 15-41.
    6. Gazis, Denos & Liu, Chiu, 2003. "Kalman filtering estimation of traffic counts for two network links in tandem," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 737-745, September.
    7. Herrera, Juan C. & Bayen, Alexandre M., 2010. "Incorporation of Lagrangian measurements in freeway traffic state estimation," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 460-481, May.
    8. Wang, Yibing & Papageorgiou, Markos, 2005. "Real-time freeway traffic state estimation based on extended Kalman filter: a general approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 141-167, February.

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