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A Bayesian method for estimating traffic flows based on plate scanning

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  • Enrique Castillo
  • Pilar Jiménez
  • José Menéndez
  • María Nogal

Abstract

In this paper a special conjugate Bayesian method, for reconstructing and estimating traffic flows, based on α-shifted-Gamma $$ \Upgamma (\theta ,\,\lambda ) $$ models $$ H(\alpha ,\,\theta ,\,\lambda ) $$ is given. If the numbers of users traveling through different routes are assumed to be independent $$ H(\alpha ,\,\theta ,\,\lambda) $$ variables with common $$ \lambda,$$ the link, origin–destination (OD) and node flows are also $$ H(\alpha ,\,\theta ,\,\lambda ) $$ random variables. We assume that the main source of information is plate scanning, which permits us to identify, totally or partially, the vehicle route, OD and link flows by scanning their corresponding plate numbers at an adequately selected subset of links. The reconstruction of the sample flows can be done exactly or approximately, depending on the intensity of the plate scanning sampling procedure. To this end a generalized least squares technique is used together with the conservation laws. A Bayesian approach using special conjugate families is proposed that allows us to estimate different traffic flows, such as route, OD-pair, scanned link or counted link flows. A detailed description of how the prior assessment, the sampling, the posterior updating and the obtention of the Bayesian distribution is given. Finally, one example of application is used to illustrate the methods and procedures. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Enrique Castillo & Pilar Jiménez & José Menéndez & María Nogal, 2013. "A Bayesian method for estimating traffic flows based on plate scanning," Transportation, Springer, vol. 40(1), pages 173-201, January.
    • Handle: RePEc:kap:transp:v:40:y:2013:i:1:p:173-201
    DOI: 10.1007/s11116-012-9443-4
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    References listed on IDEAS

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    Cited by:

    1. Jincheng Jiang & Nico Dellaert & Tom Van Woensel & Lixin Wu, 2020. "Modelling traffic flows and estimating road travel times in transportation network under dynamic disturbances," Transportation, Springer, vol. 47(6), pages 2951-2980, December.
    2. Damla Ahipaşaoğlu, Selin & Arıkan, Uğur & Natarajan, Karthik, 2016. "On the flexibility of using marginal distribution choice models in traffic equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 130-158.
    3. Abdullah Alshehri & Mahmoud Owais & Jayadev Gyani & Mishal H. Aljarbou & Saleh Alsulamy, 2023. "Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    4. Selin Damla Ahipaşaoğlu & Uğur Arıkan & Karthik Natarajan, 2019. "Distributionally Robust Markovian Traffic Equilibrium," Transportation Science, INFORMS, vol. 53(6), pages 1546-1562, November.

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