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Estimation of dynamic origin-destination trip tables for a general network

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  • Sherali, Hanif D.
  • Park, Taehyung

Abstract

In this paper, we propose a parametric optimization approach to estimate time-dependent path flows, or origin-destination (OD) trip tables, using available data on link traffic volumes for a general road network. Our model assumes knowledge of certain time-dependent link flow contribution factors that are a dynamic generalization of the path-link incidence matrix for the static case. A least squares model is accordingly formulated to determine the time-dependent trip tables. We develop a column generation approach that uses a sequence of dynamic shortest path subproblems in order to solve this problem. Computational results are presented on several variants of two sample test networks from the literature.

Suggested Citation

  • Sherali, Hanif D. & Park, Taehyung, 2001. "Estimation of dynamic origin-destination trip tables for a general network," Transportation Research Part B: Methodological, Elsevier, vol. 35(3), pages 217-235, March.
    • Handle: RePEc:eee:transb:v:35:y:2001:i:3:p:217-235
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    References listed on IDEAS

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    1. 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.
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    5. Sherali, Hanif D. & Sivanandan, R. & Hobeika, Antoine G., 1994. "A linear programming approach for synthesizing origin-destination trip tables from link traffic volumes," Transportation Research Part B: Methodological, Elsevier, vol. 28(3), pages 213-233, June.
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    Cited by:

    1. Anselmo Ramalho Pitombeira-Neto & Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho, 2020. "A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks," Networks and Spatial Economics, Springer, vol. 20(2), pages 499-527, June.
    2. 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.
    3. Zhao, Miyuan & Chow, Joseph Y.J. & Ritchie, Stephen G., 2015. "An inventory-based simulation model for annual-to-daily temporal freight assignment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 83-101.
    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. Kumarage, Sakitha & Yildirimoglu, Mehmet & Zheng, Zuduo, 2023. "A hybrid modelling framework for the estimation of dynamic origin–destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    6. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    7. Kumar, Anshuman Anjani & Kang, Jee Eun & Kwon, Changhyun & Nikolaev, Alexander, 2016. "Inferring origin-destination pairs and utility-based travel preferences of shared mobility system users in a multi-modal environment," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 270-291.
    8. Hänseler, Flurin S. & Bierlaire, Michel & Farooq, Bilal & Mühlematter, Thomas, 2014. "A macroscopic loading model for time-varying pedestrian flows in public walking areas," Transportation Research Part B: Methodological, Elsevier, vol. 69(C), pages 60-80.
    9. Gomes, Gabriel C., 2004. "Optimization and Microsimulation of On-ramp Metering for Congested Freeways," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt95k1q411, Institute of Transportation Studies, UC Berkeley.
    10. 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.
    11. Chow, Joseph Y.J. & Ritchie, Stephen G. & Jeong, Kyungsoo, 2014. "Nonlinear inverse optimization for parameter estimation of commodity-vehicle-decoupled freight assignment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 71-91.
    12. Yu Nie & H. Zhang, 2010. "A Relaxation Approach for Estimating Origin–Destination Trip Tables," Networks and Spatial Economics, Springer, vol. 10(1), pages 147-172, March.
    13. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
    14. Lundgren, Jan T. & Peterson, Anders, 2008. "A heuristic for the bilevel origin-destination-matrix estimation problem," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 339-354, May.
    15. 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.
    16. Felipe Zúñiga & Juan Carlos Muñoz & Ricardo Giesen, 2021. "Estimation and prediction of dynamic matrix travel on a public transport corridor using historical data and real-time information," Public Transport, Springer, vol. 13(1), pages 59-80, March.
    17. Zhang, Michael & Ma, Jingtao, 2008. "Developing Calibration Tools for Microscopic Traffic Simulation Final Report Part 1: Overview Methods and Guidelines on Project Scoping and Data Collection," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3hh8f7nz, Institute of Transportation Studies, UC Berkeley.

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