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Estimation of origin–destination trip rates in Leicester

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  • Martin L. Hazelton

Abstract

The road system in region RA of Leicester has vehicle detectors embedded in many of the network's road links. Vehicle counts from these detectors can provide transportation researchers with a rich source of data. However, for many projects it is necessary for researchers to have an estimate of origin‐to‐destination vehicle flow rates. Obtaining such estimates from data observed on individual road links is a non‐trivial statistical problem, made more difficult in the present context by non‐negligible measurement errors in the vehicle counts collected. The paper uses road link traffic count data from April 1994 to estimate the origin–destination flow rates for region RA. A model for the error prone traffic counts is developed, but the resulting likelihood is not available in closed form. Nevertheless, it can be smoothly approximated by using Monte Carlo integration. The approximate likelihood is combined with prior information from a May 1991 survey in a Bayesian framework. The posterior is explored using the Hastings–Metropolis algorithm, since its normalizing constant is not available. Preliminary findings suggest that the data are overdispersed according to the original model. Results for a revised model indicate that a degree of overdispersion exists, but that the estimates of origin–destination flow rates are quite insensitive to the change in model specification.

Suggested Citation

  • Martin L. Hazelton, 2001. "Estimation of origin–destination trip rates in Leicester," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 423-433.
    • Handle: RePEc:bla:jorssc:v:50:y:2001:i:4:p:423-433
    DOI: 10.1111/1467-9876.00245
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    Cited by:

    1. Hazelton, Martin L. & Karimi, Masoud, 2024. "When lattice bases are Markov bases," Statistics & Probability Letters, Elsevier, vol. 209(C).
    2. Hazelton, Martin L., 2008. "Statistical inference for time varying origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 42(6), pages 542-552, July.
    3. Blume, Steffen O.P. & Corman, Francesco & Sansavini, Giovanni, 2022. "Bayesian origin-destination estimation in networked transit systems using nodal in- and outflow counts," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 60-94.
    4. Hazelton, Martin L., 2010. "Bayesian inference for network-based models with a linear inverse structure," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 674-685, June.
    5. Hazelton, Martin L., 2003. "Some comments on origin-destination matrix estimation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(10), pages 811-822, December.
    6. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.

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