IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v10y2018i1d10.1007_s12469-017-0171-1.html
   My bibliography  Save this article

Application of smart card data in validating a large-scale multi-modal transit assignment model

Author

Listed:
  • Ahmad Tavassoli

    (The University of Queensland)

  • Mahmoud Mesbah

    (Amirkabir University of Technology)

  • Mark Hickman

    (The University of Queensland)

Abstract

The accuracy of transit assignment plays an important role in the successful design and operation of a transit system. The majority of previous studies on validating transit assignment models has used limited survey data or has lacked a large-scale multimodal and high quality dataset. Considering the advantages of smart card [automatic fare collection (AFC)] systems, the aims of this study are to put forward a methodological framework to validate existing transit assignment models and to quantify the performance of these models. Our study combines data from three sources: the General Transit Feed Specification, an AFC system, and a strategic transport model from a large-scale multimodal public transport network, namely the South-East Queensland (SEQ) network in Australia. The AFC system in SEQ has provided a very large and highly accurate dataset on passenger boardings and alightings for the three transit modes of bus, rail and ferry. Following a data analysis, an origin–destination trip matrix is estimated for the AM peak period using AFC data as an input to the transit assignment model. Then, the results of the transit assignment model are compared with the actual passengers’ route choices over the same period, at different levels of aggregation. The model performance is quantified by each route (and direction), by each segment of each route (and direction), and by each stop. The results indicate that relatively tighter thresholds are required to validate the transit assignment at the segment level than at the stop level. Furthermore, the validation results indicate that the greatest error is realized for the bus mode, while the level of accuracy in the rail mode is the best. The results suggest a segment-level analysis should be used as the most useful level of aggregation for future calibration and validation of transit assignment models.

Suggested Citation

  • Ahmad Tavassoli & Mahmoud Mesbah & Mark Hickman, 2018. "Application of smart card data in validating a large-scale multi-modal transit assignment model," Public Transport, Springer, vol. 10(1), pages 1-21, May.
    • Handle: RePEc:spr:pubtra:v:10:y:2018:i:1:d:10.1007_s12469-017-0171-1
    DOI: 10.1007/s12469-017-0171-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-017-0171-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12469-017-0171-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Spiess, Heinz & Florian, Michael, 1989. "Optimal strategies: A new assignment model for transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 23(2), pages 83-102, April.
    2. Cepeda, M. & Cominetti, R. & Florian, M., 2006. "A frequency-based assignment model for congested transit networks with strict capacity constraints: characterization and computation of equilibria," Transportation Research Part B: Methodological, Elsevier, vol. 40(6), pages 437-459, July.
    3. Lam, W. H. K. & Gao, Z. Y. & Chan, K. S. & Yang, H., 1999. "A stochastic user equilibrium assignment model for congested transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 33(5), pages 351-368, June.
    4. Nielsen, Otto Anker, 2000. "A stochastic transit assignment model considering differences in passengers utility functions," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 377-402, June.
    5. Goran Vuk & Christian Hansen, 2006. "Validating the Passenger Traffic Model for Copenhagen," Transportation, Springer, vol. 33(4), pages 371-392, July.
    6. 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.
    7. Neema Nassir & Mark Hickman & Zhen-Liang Ma, 2015. "Activity detection and transfer identification for public transit fare card data," Transportation, Springer, vol. 42(4), pages 683-705, July.
    8. Mark D. Hickman & David H. Bernstein, 1997. "Transit Service and Path Choice Models in Stochastic and Time-Dependent Networks," Transportation Science, INFORMS, vol. 31(2), pages 129-146, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Filip Covic & Stefan Voß, 2019. "Interoperable smart card data management in public mass transit," Public Transport, Springer, vol. 11(3), pages 523-548, October.
    2. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmad Tavassoli & Mahmoud Mesbah & Mark Hickman, 2020. "Calibrating a transit assignment model using smart card data in a large-scale multi-modal transit network," Transportation, Springer, vol. 47(5), pages 2133-2156, October.
    2. Hamdouch, Younes & Szeto, W.Y. & Jiang, Y., 2014. "A new schedule-based transit assignment model with travel strategies and supply uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 35-67.
    3. Khani, Alireza, 2019. "An online shortest path algorithm for reliable routing in schedule-based transit networks considering transfer failure probability," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 549-564.
    4. Wang, Zhichao & Jiang, Rui & Jiang, Yu & Gao, Ziyou & Liu, Ronghui, 2024. "Modelling bus bunching along a common line corridor considering passenger arrival time and transfer choice under stochastic travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    5. Li, Guoyuan & Chen, Anthony, 2023. "Strategy-based transit stochastic user equilibrium model with capacity and number-of-transfers constraints," European Journal of Operational Research, Elsevier, vol. 305(1), pages 164-183.
    6. Jiang, Y. & Szeto, W.Y., 2016. "Reliability-based stochastic transit assignment: Formulations and capacity paradox," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 181-206.
    7. Sun, S. & Szeto, W.Y., 2018. "Logit-based transit assignment: Approach-based formulation and paradox revisit," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 191-215.
    8. Wu, Di & Yin, Yafeng & Lawphongpanich, Siriphong, 2011. "Pareto-improving congestion pricing on multimodal transportation networks," European Journal of Operational Research, Elsevier, vol. 210(3), pages 660-669, May.
    9. Hamdouch, Younes & Lawphongpanich, Siriphong, 2008. "Schedule-based transit assignment model with travel strategies and capacity constraints," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 663-684, August.
    10. Wen Hua & Ghim Ping Ong, 2018. "Effect of information contagion during train service disruption for an integrated rail-bus transit system," Public Transport, Springer, vol. 10(3), pages 571-594, December.
    11. Ren, Hualing & Song, Yingjie & Long, Jiancheng & Si, Bingfeng, 2021. "A new transit assignment model based on line and node strategies," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 121-142.
    12. Cancela, Héctor & Mauttone, Antonio & Urquhart, María E., 2015. "Mathematical programming formulations for transit network design," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 17-37.
    13. 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.
    14. Z. Wu & W. Lam, 2006. "Transit passenger origin-destination estimation in congested transit networks with elastic line frequencies," Annals of Operations Research, Springer, vol. 144(1), pages 363-378, April.
    15. Szeto, W.Y. & Jiang, Y., 2014. "Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 235-263.
    16. Du, Muqing & Chen, Anthony, 2022. "Sensitivity analysis for transit equilibrium assignment and applications to uncertainty analysis," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 175-202.
    17. Canca, David & Andrade-Pineda, José Luis & De los Santos, Alicia & Calle, Marcos, 2018. "The Railway Rapid Transit frequency setting problem with speed-dependent operation costs," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 494-519.
    18. Kenetsu Uchida & Agachai Sumalee & David Watling & Richard Connors, 2007. "A Study on Network Design Problems for Multi-modal Networks by Probit-based Stochastic User Equilibrium," Networks and Spatial Economics, Springer, vol. 7(3), pages 213-240, September.
    19. Hamdouch, Younes & Ho, H.W. & Sumalee, Agachai & Wang, Guodong, 2011. "Schedule-based transit assignment model with vehicle capacity and seat availability," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1805-1830.
    20. Lo, Hong K. & Yip, C. W. & Wan, K. H., 2003. "Modeling transfer and non-linear fare structure in multi-modal network," Transportation Research Part B: Methodological, Elsevier, vol. 37(2), pages 149-170, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pubtra:v:10:y:2018:i:1:d:10.1007_s12469-017-0171-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.