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On the recursive estimation of vehicular speed using data from a single inductance loop detector: A Bayesian approach

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  • Li, Baibing

Abstract

This paper investigates the recursive estimation of vehicular speed using the information provided by a single inductance loop detector (ILD). A statistical model for space-mean speed measured by an ILD is developed, upon which a Bayesian analysis is carried out to estimate vehicular speed. This results in a set of recursive formulae which is analytically nice and neat. The incurred computational cost for updating the estimate of vehicular speed is kept to be a minimum. As a by-product, a simple method for the calibration of the effective vehicle length of an ILD is also developed. The proposed method is illustrated using simulation studies and a practical example.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transb:v:43:y:2009:i:4:p:391-402
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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. 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. 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.
    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.

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