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Kalman filtering estimation of traffic counts for two network links in tandem

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  • Gazis, Denos
  • Liu, Chiu

Abstract

Estimating accurately the vehicular density along roads is very important for managing and controlling traffic operations in urban networks. The vehicular density information may be derived from raw counts by loop detectors or other detection devices. However, these counts are subject to errors, which can degrade considerably the density estimates. The extended Kalman filter (EKF) has been applied in the past for obtaining improved density estimates, by coupling the detector counts with independent density estimates, subject to uncorrelated errors. In this paper, the EKF is applied for estimating vehicle counts for two roadway sections in tandem. Because measurement errors at the joint of the two sections are shared by both sections, the resulting count estimates are improved over those obtained by treating the two sections as isolated. This is confirmed by comparing the analytical derivations of the error estimate when treating two tandem sections together with those obtained by treating the sections as isolated ones.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:37:y:2003:i:8:p:737-745
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    References listed on IDEAS

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    1. 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.
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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. Panda, Manoj & Ngoduy, Dong & Vu, Hai L., 2019. "Multiple model stochastic filtering for traffic density estimation on urban arterials," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 280-306.
    3. Tuo Sun & Shihao Zhu & Ruochen Hao & Bo Sun & Jiemin Xie, 2022. "Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms," Mathematics, MDPI, vol. 10(14), pages 1-22, 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.

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