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Recursive estimation based on the equality-constrained optimization for intersection origin-destination matrices

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  • Li, Baibing
  • De Moor, Bart

Abstract

A fast constrained recursive identification (CRI) algorithm is proposed to estimate intersection origin-destination (O-D) matrices dynamically. The basic idea of the CRI algorithm is to estimate intersection O-D matrices based on equality-constrained optimization and then to adjust them by Bell's correction (Bell, 1991a. The estimation of origin-destination matrices by constrained generalized least squares. Transporation Research 25B, 13-22; Bell, 1991b. The real-time estimation of origin-destination flows in the presence of platoon dispersion. Transportation Research 25B, 115-125.) for inequality constraints. Numerical results show that the accuracy of estimates by the CRI algorithm is fairly good--the solutions obtained by the CRI are optimal in majority of the cases, while the computational efforts are very limited--increment mainly lies on the evaluation of an inverse for an mxm matrix (m=4 for a typical intersection) compared with the ordinary recursive least squares method. These results mean that a properly designed recursive algorithm can indeed avoid iterative procedure in each time step to obtain highly accurate on-line estimates for intersection O-D matrices. Therefore, the CRI algorithm with its reasonable balance between accuracy and computational simplicity is very suitable for practical use.

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  • Li, Baibing & De Moor, Bart, 1999. "Recursive estimation based on the equality-constrained optimization for intersection origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 33(3), pages 203-214, April.
    • Handle: RePEc:eee:transb:v:33:y:1999:i:3:p:203-214
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    References listed on IDEAS

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    1. Nancy L. Nihan & Gary A. Davis, 1989. "Application of Prediction-Error Minimization and Maximum Likelihood to Estimate Intersection O-D Matrices from Traffic Counts," Transportation Science, INFORMS, vol. 23(2), pages 77-90, May.
    2. Nihan, Nancy L. & Davis, Gary A., 1987. "Recursive estimation of origin-destination matrices from input/output counts," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 149-163, April.
    3. Cremer, M. & Keller, H., 1987. "A new class of dynamic methods for the identification of origin-destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 117-132, April.
    4. Sherali, Hanif D. & Arora, Namita & Hobeika, Antoine G., 1997. "Parameter optimization methods for estimating dynamic origin-destination trip-tables," Transportation Research Part B: Methodological, Elsevier, vol. 31(2), pages 141-157, April.
    5. Bell, Michael G. H., 1991. "The real time estimation of origin-destination flows in the presence of platoon dispersion," Transportation Research Part B: Methodological, Elsevier, vol. 25(2-3), pages 115-125.
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    1. Bierlaire, Michel, 2002. "The total demand scale: a new measure of quality for static and dynamic origin-destination trip tables," Transportation Research Part B: Methodological, Elsevier, vol. 36(9), pages 837-850, November.
    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. Li, Baibing, 2009. "Markov models for Bayesian analysis about transit route origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 43(3), pages 301-310, March.
    4. Li, Baibing & Moor, Bart De, 2002. "Dynamic identification of origin-destination matrices in the presence of incomplete observations," Transportation Research Part B: Methodological, Elsevier, vol. 36(1), pages 37-57, January.

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