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Recursive estimation of origin-destination matrices from input/output counts

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  • Nihan, Nancy L.
  • Davis, Gary A.

Abstract

The application of recursive prediction error techniques to the problem of estimating origin-destination patterns from input and output volume counts is described. Each algorithm deals with the special case where route choice between origin and destination can be ignored. A gradient algorithm developed by Cremer and Keller (1983) turns out to be a special case of a family of methods described by Ljung and Söderström (1983). After describing how the methods developed in Ljung and Söderström (1983) could be modified so that the resulting estimates satisfy natural constraints, a number of algorithm possibilities are tested. Generally, those algorithms employing Gauss-Newton search directions appear superior to gradient-based methods, while the constraining procedures improve accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:21:y:1987:i:2:p:149-163
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    Cited by:

    1. Lin, Pei-Wei & Chang, Gang-Len, 2007. "A generalized model and solution algorithm for estimation of the dynamic freeway origin-destination matrix," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 554-572, June.
    2. Chao Sun & Yulin Chang & Yuji Shi & Lin Cheng & Jie Ma, 2019. "Subnetwork Origin-Destination Matrix Estimation Under Travel Demand Constraints," Networks and Spatial Economics, Springer, vol. 19(4), pages 1123-1142, December.
    3. 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.
    4. Nie, Yu (Marco) & Zhang, H.M., 2008. "A variational inequality formulation for inferring dynamic origin-destination travel demands," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 635-662, August.
    5. Sun, Carlos & Porwal, Himanshu, 2000. "Dynamic Origin/Destination Estimation Using True Section Densities," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0f0711s6, Institute of Transportation Studies, UC Berkeley.
    6. K. Ashok & M. E. Ben-Akiva, 2000. "Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin–Destination Flows," Transportation Science, INFORMS, vol. 34(1), pages 21-36, February.
    7. Hjorth, U., 1999. "The inherent precision of regression estimated route probabilities," Transportation Research Part B: Methodological, Elsevier, vol. 33(8), pages 593-607, November.
    8. Chang, Gang-Len & Tao, Xianding, 1999. "An integrated model for estimating time-varying network origin-destination distributions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(5), pages 381-399, June.
    9. Ritchie, Stephen & Sun, Carlos, 1998. "Section Related Measures of Traffic System Performance: Final Report," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4sc0t3bv, Institute of Transportation Studies, UC Berkeley.
    10. 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.
    11. 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.
    12. 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.
    13. K. Ashok & M. E. Ben-Akiva, 2002. "Estimation and Prediction of Time-Dependent Origin-Destination Flows with a Stochastic Mapping to Path Flows and Link Flows," Transportation Science, INFORMS, vol. 36(2), pages 184-198, May.
    14. Zhang, Michael & Nie, Yu & Shen, Wei & Lee, Ming S. & Jansuwan, Sarawut & Chootinan, Piya & Pravinvongvuth, Surachet & Chen, Anthony & Recker, Will W., 2008. "Development of A Path Flow Estimator for Inferring Steady-State and Time-Dependent Origin-Destination Trip Matrices," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3nr033sc, Institute of Transportation Studies, UC Berkeley.
    15. Garcia, Reinaldo C., 2003. "Implementing a Kalman Filtering Dynamic O-D Algorithm within Paramics- Analysing Quadstone Won Efforts for the Dynamic O-D Estimation Problem," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6vf61301, Institute of Transportation Studies, UC Berkeley.
    16. Garcia, Reinaldo C., 2002. "Implementing A Dynamic O-D Estimation Algorithm within the Microscopic Traffic Simulator Paramics," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0n62j6nq, Institute of Transportation Studies, UC Berkeley.
    17. Wu, Jifeng & Chang, Gang-Len, 1996. "Estimation of time-varying origin-destination distributions with dynamic screenline flows," Transportation Research Part B: Methodological, Elsevier, vol. 30(4), pages 277-290, August.
    18. Hjorth, Urban, 2002. "Traffic subflow estimation and bootstrap analysis from filtered counts," Transportation Research Part B: Methodological, Elsevier, vol. 36(4), pages 345-359, May.
    19. 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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