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A statistical procedure for estimating a mean origin-destination matrix from a partial registration plate survey

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  • Watling, David P.
  • Maher, Michael J.

Abstract

This paper addresses the problem of matching partial registration plate data collected at a number of origins and destinations in a traffic system, the aim being to reconstruct the pattern of vehicle movements. The matching algorithm proposed here embraces two of the most appealing features of the methods used in most commercially available packages -- the use of passage times to improve the accuracy of the matching process and the ability to handle data at any number of origins and destinations -- but does so within a statistical framework. The new method is seen to have a number of significant advantages over existing approaches. A model is firstly proposed for the two observation point case and the algorithm derived, in which, alternately, the most probable complete matching consistent with the observed data and the maximum is described. The approach is then generalised to handle data collected at any number of origins and destinations. Finally, the performance of the proposed method is studied in applications to simulated data sets, where the solution yielded by the algorithm is compared with the (known) true matching of the data.

Suggested Citation

  • Watling, David P. & Maher, Michael J., 1992. "A statistical procedure for estimating a mean origin-destination matrix from a partial registration plate survey," Transportation Research Part B: Methodological, Elsevier, vol. 26(3), pages 171-193, June.
    • Handle: RePEc:eee:transb:v:26:y:1992:i:3:p:171-193
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    Citations

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    Cited by:

    1. Castillo, Enrique & Menéndez, José María & Jiménez, Pilar, 2008. "Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 455-481, June.
    2. Guo, Jianhua & Liu, Yu & Li, Xiugang & Huang, Wei & Cao, Jinde & Wei, Yun, 2019. "Enhanced least square based dynamic OD matrix estimation using Radio Frequency Identification data," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 27-40.
    3. Siripirote, Treerapot & Sumalee, Agachai & Ho, H.W. & Lam, William H.K., 2015. "Statistical approach for activity-based model calibration based on plate scanning and traffic counts data," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 280-300.
    4. Hjorth, U., 1999. "The inherent precision of regression estimated route probabilities," Transportation Research Part B: Methodological, Elsevier, vol. 33(8), pages 593-607, November.
    5. 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.
    6. Zhang, Xiaoyan & Maher, Mike J., 1998. "The evaluation and application of a fully disaggregate method for trip matrix estimation with platoon dispersion," Transportation Research Part B: Methodological, Elsevier, vol. 32(4), pages 261-276, May.
    7. Clegg, Richard G., 2006. "A set theoretic framework for enumerating matches in surveys and its application to reducing inaccuracies in vehicle roadside surveys," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1501-1513, December.

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