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Estimation of time-dependent origin-destination matrices for transit networks

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  • Wong, S. C.
  • Tong, C. O.

Abstract

In this paper, the estimation of time-dependent origin-destination (O-D) matrices for transit network based on observed passenger counts is given. The dynamic assignment framework is based on a schedule-based transit network model, which can help determine the time-dependent least cost paths between all O-D pairs, and for each of them the clock arrival times at the end nodes of all observed links (if any) in the transit network. An entropy-based approach is then employed to estimate the time-dependent O-D matrices based on the observed passenger counts at those observed links in the network. An efficient sparse algorithm is also proposed to solve the resulting mathematical programming problem. The estimation methodology is tested in a transit network from the Mass Transit Railway (MTR) system in Hong Kong which is one of the busiest railway systems in the world. Both cases with and without prior information of the O-D matrices are considered for this network. The predicted matrices are then compared with the true matrices obtained from a sophisticated electronic fare collection system of MTR. Good agreement between predicted and observed matrices are found.

Suggested Citation

  • Wong, S. C. & Tong, C. O., 1998. "Estimation of time-dependent origin-destination matrices for transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 32(1), pages 35-48, January.
    • Handle: RePEc:eee:transb:v:32:y:1998:i:1:p:35-48
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    References listed on IDEAS

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    1. Tong, C.O. & Wong, S.C., 1998. "A stochastic transit assignment model using a dynamic schedule-based network," Transportation Research Part B: Methodological, Elsevier, vol. 33(2), pages 107-121, April.
    2. Li, Guoyuan & Chen, Anthony, 2022. "Frequency-based path flow estimator for transit origin-destination trip matrices incorporating automatic passenger count and automatic fare collection data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    3. Flurin S. Hänseler & Nicholas A. Molyneaux & Michel Bierlaire, 2017. "Estimation of Pedestrian Origin-Destination Demand in Train Stations," Transportation Science, INFORMS, vol. 51(3), pages 981-997, August.
    4. Agostino Nuzzolo & Francesco Russo & Umberto Crisalli, 2001. "A Doubly Dynamic Schedule-based Assignment Model for Transit Networks," Transportation Science, INFORMS, vol. 35(3), pages 268-285, August.
    5. Hänseler, Flurin S. & Bierlaire, Michel & Scarinci, Riccardo, 2016. "Assessing the usage and level-of-service of pedestrian facilities in train stations: A Swiss case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 106-123.
    6. Poon, M. H. & Wong, S. C. & Tong, C. O., 2004. "A dynamic schedule-based model for congested transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 343-368, May.
    7. Androutsopoulos, Konstantinos N. & Zografos, Konstantinos G., 2009. "Solving the multi-criteria time-dependent routing and scheduling problem in a multimodal fixed scheduled network," European Journal of Operational Research, Elsevier, vol. 192(1), pages 18-28, January.
    8. Abderrahman Ait-Ali & Jonas Eliasson, 2022. "The value of additional data for public transport origin–destination matrix estimation," Public Transport, Springer, vol. 14(2), pages 419-439, June.
    9. 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.
    10. Sun, Ran & Fan, Yueyue, 2024. "Stochastic OD demand estimation using stochastic programming," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    11. Diana P. Moreno-Palacio & Carlos A. Gonzalez-Calderon & John Jairo Posada-Henao & Hector Lopez-Ospina & Jhan Kevin Gil-Marin, 2022. "Entropy-Based Transit Tour Synthesis Using Fuzzy Logic," Sustainability, MDPI, vol. 14(21), pages 1-25, November.
    12. Chen, Kang & Yang, Zhongzhen & Notteboom, Theo, 2014. "The design of coastal shipping services subject to carbon emission reduction targets and state subsidy levels," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 192-211.
    13. Tangjian Wei & Feng Shi & Guangming Xu, 2019. "Estimation of Time-Varying Passenger Demand for High Speed Rail System," Complexity, Hindawi, vol. 2019, pages 1-24, March.
    14. Häme, Lauri & Hakula, Harri, 2013. "Dynamic journeying under uncertainty," European Journal of Operational Research, Elsevier, vol. 225(3), pages 455-471.
    15. Yang Chen & Shu Yang & Mengqi Hu & Yao-Jan Wu, 2016. "A reliability-based transit trip planning model under transit network uncertainty," Public Transport, Springer, vol. 8(3), pages 477-496, December.

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