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Using traffic assignment models to assist Bayesian inference for origin–destination matrices

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  • Hazelton, Martin L.
  • Najim, Lara

Abstract

Estimation of traffic volumes between each origin and destination of travel is a standard practice in transport engineering. Commonly the available data constitute traffic counts at various locations on the network, supplemented by a survey-based prior estimate of mean origin–destination traffic volumes. Statistical inference in this type of network tomography problem is known to be challenging. Moreover, the difficulties are increased in practice by the presence of a large number of nuisance parameters corresponding to route choice probabilities, for which we have no direct prior information. Working in a Bayesian framework, we determine these parameters using a stochastic user equilibrium route choice model. We develop an MCMC algorithm for model fitting. This requires repeated computation of stochastic user equilibrium flows, and so we develop a computationally cheap emulator. Our methods are tested on numerical examples based on a section of the road network in the English city of Leicester.

Suggested Citation

  • Hazelton, Martin L. & Najim, Lara, 2024. "Using traffic assignment models to assist Bayesian inference for origin–destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:transb:v:186:y:2024:i:c:s0191261524001437
    DOI: 10.1016/j.trb.2024.103019
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    References listed on IDEAS

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