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A generalized and efficient algorithm for estimating transit route ODs from passenger counts

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  • Li, Yuwei
  • Cassidy, Michael J.

Abstract

An algorithm is presented for transit passenger OD estimation. The algorithm, like some other existing OD estimation techniques, generates estimates based upon passenger boarding and alighting counts at each stop along the route. The algorithm is distinct in that it does not only estimate an OD matrix for the vehicle trip from which the boarding and alighting counts were taken. Rather, it further estimates the passenger alighting probabilities at every stop on the route and these are more apt to remain fixed across transit trips. Therefore, when coupled with projected boarding counts, the alighting probabilities better characterize OD patterns on the route. These probabilities, moreover, are estimated in such way as to reflect the passengers' latent tendencies to travel to and from "major activity centers" where trip-making is induced. The algorithm is therefore a more general-use method than is the estimation technique proposed by Tsygalnitsky. Since the algorithm does not require a seed matrix, and since the number of iterations required for generating estimates is specified a priori, the algorithm is easier to apply and more computationally efficient than the balancing method of OD estimation.

Suggested Citation

  • Li, Yuwei & Cassidy, Michael J., 2007. "A generalized and efficient algorithm for estimating transit route ODs from passenger counts," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 114-125, January.
  • Handle: RePEc:eee:transb:v:41:y:2007:i:1:p:114-125
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    References listed on IDEAS

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    1. Lamond, B. & Stewart, N. F., 1981. "Bregman's balancing method," Transportation Research Part B: Methodological, Elsevier, vol. 15(4), pages 239-248, August.
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    Cited by:

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    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. Yun Wang & Faiz Currim & Sudha Ram, 2022. "Deep Learning of Spatiotemporal Patterns for Urban Mobility Prediction Using Big Data," Information Systems Research, INFORMS, vol. 33(2), pages 579-598, June.
    4. Jin, Meihan & Wang, Menghan & Gong, Yongxi & Liu, Yu, 2022. "Spatio-temporally constrained origin–destination inferring using public transit fare card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    5. Juha-Matti Kuusinen & Janne Sorsa & Marja-Liisa Siikonen, 2015. "The Elevator Trip Origin-Destination Matrix Estimation Problem," Transportation Science, INFORMS, vol. 49(3), pages 559-576, August.
    6. 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.
    7. 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.
    8. 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.
    9. 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.

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