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Bayesian inference for vehicle speed and vehicle length using dual-loop detector data

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  • Li, Baibing

Abstract

A dual-loop detector consists of two connected single-loop detectors placed several feet apart. Compared with a single-loop detector, it is able to provide more useful information on traffic flow with a higher precision. In this paper we investigate statistical inference for vehicle speed and vehicle length using dual-loop detector data. A Bayesian analysis is performed to combine current observations on traffic flow with prior knowledge, which results in a set of simple formulas for the online estimation of both vehicle speed and vehicle length. As a by-product, vehicle classification is also investigated on the basis of posterior classification probabilities. The computational overhead of updating the estimates is kept to a minimum when new information on traffic flow becomes available. The method is illustrated using real traffic data.

Suggested Citation

  • Li, Baibing, 2010. "Bayesian inference for vehicle speed and vehicle length using dual-loop detector data," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 108-119, January.
    • Handle: RePEc:eee:transb:v:44:y:2010:i:1:p:108-119
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    References listed on IDEAS

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    1. Andrew Kurkjian & Stanley B. Gershwin & Paul K. Houpt & Alan S. Willsky & E. Y. Chow & C. S. Greene, 1980. "Estimation of Roadway Traffic Density on Freeways Using Presence Detector Data," Transportation Science, INFORMS, vol. 14(3), pages 232-261, August.
    2. Li, Baibing, 2009. "On the recursive estimation of vehicular speed using data from a single inductance loop detector: A Bayesian approach," Transportation Research Part B: Methodological, Elsevier, vol. 43(4), pages 391-402, May.
    3. Dailey, D. J., 1999. "A statistical algorithm for estimating speed from single loop volume and occupancy measurements," Transportation Research Part B: Methodological, Elsevier, vol. 33(5), pages 313-322, June.
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    Cited by:

    1. Comert, Gurcan, 2016. "Queue length estimation from probe vehicles at isolated intersections: Estimators for primary parameters," European Journal of Operational Research, Elsevier, vol. 252(2), pages 502-521.
    2. Wong, Wai & Shen, Shengyin & Zhao, Yan & Liu, Henry X., 2019. "On the estimation of connected vehicle penetration rate based on single-source connected vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 169-191.
    3. Wang, Zhengli & Zhu, Liyun & Ran, Bin & Jiang, Hai, 2020. "Queue profile estimation at a signalized intersection by exploiting the spatiotemporal propagation of shockwaves," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 59-71.
    4. Li, Baibing, 2017. "Stochastic modeling for vehicle platoons (I): Dynamic grouping behavior and online platoon recognition," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 364-377.

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