IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v179y2024ics096585642300349x.html
   My bibliography  Save this article

Public transport route choice modelling: Reducing estimation bias when using smart card data

Author

Listed:
  • Ingvardson, Jesper Bláfoss
  • Thorhauge, Mikkel
  • Nielsen, Otto Anker
  • Eltved, Morten

Abstract

Automated Fare Collection (AFC) data for public transport analyses has received much research interest recently, including its use for the estimation of passenger route choice preferences. However, an important problem persists since AFC data only includes information about the trip within the public transport system, that is stop-to-stop (tap-in to tap-out). Not knowing the full trip from door-to-door might lead to estimation bias, especially when estimating route choice models based on only the chosen stops, which is common practice in current research using AFC data. To avoid this, we propose an improved method for estimating route choice models in public transport using AFC data. The method is based on randomly generating pseudo origin (and destination) points in close vicinity of the actually chosen origin (and destination) stops, thus allowing pseudo access and egress times to be incorporated into the route choice model. The framework is compatible with any probability density function. We suggest using the Beta distribution for generating points when knowledge about access and egress distances are available, whereas the Uniform distribution is suggested when no knowledge is available. The method was applied on replicated AFC data based on traditional travel survey data from the Greater Copenhagen area in Denmark. The results of the model estimations confirm estimation bias in parameter estimates when not correcting for the lack of access/egress information. The proposed method notably improves in-vehicle-time parameter estimates of the route choice model compared to estimation assuming AFC stop-to-stop data, whereas access/egress time and hidden waiting time parameters are still biased, although to a lesser extent than a traditional naïve estimation based on stop-to-stop data.

Suggested Citation

  • Ingvardson, Jesper Bláfoss & Thorhauge, Mikkel & Nielsen, Otto Anker & Eltved, Morten, 2024. "Public transport route choice modelling: Reducing estimation bias when using smart card data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
  • Handle: RePEc:eee:transa:v:179:y:2024:i:c:s096585642300349x
    DOI: 10.1016/j.tra.2023.103929
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096585642300349X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2023.103929?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. Raveau, Sebastián & Muñoz, Juan Carlos & de Grange, Louis, 2011. "A topological route choice model for metro," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 138-147, February.
    2. Nielsen, Otto Anker & Eltved, Morten & Anderson, Marie Karen & Prato, Carlo Giacomo, 2021. "Relevance of detailed transfer attributes in large-scale multimodal route choice models for metropolitan public transport passengers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 76-92.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    4. Nassir, Neema & Hickman, Mark & Ma, Zhen-Liang, 2019. "A strategy-based recursive path choice model for public transit smart card data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 528-548.
    5. Ikki Kim & Hyoung-Chul Kim & Dong-Jeong Seo & Jung In Kim, 2020. "Calibration of a transit route choice model using revealed population data of smartcard in a multimodal transit network," Transportation, Springer, vol. 47(5), pages 2179-2202, October.
    6. Marie Karen Anderson & Otto Anker Nielsen & Carlo Giacomo Prato, 2017. "Multimodal route choice models of public transport passengers in the Greater Copenhagen Area," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 221-245, September.
    7. Hörcher, Daniel & Graham, Daniel J. & Anderson, Richard J., 2017. "Crowding cost estimation with large scale smart card and vehicle location data," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 105-125.
    8. Piet Bovy & Sascha Hoogendoorn-Lanser, 2005. "Modelling route choice behaviour in multi-modal transport networks," Transportation, Springer, vol. 32(4), pages 341-368, July.
    9. 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.
    10. Gopalakrishnan, Raja & Guevara, C. Angelo & Ben-Akiva, Moshe, 2020. "Combining multiple imputation and control function methods to deal with missing data and endogeneity in discrete-choice models," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 45-57.
    Full references (including those not matched with items on IDEAS)

    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. Nielsen, Otto Anker & Eltved, Morten & Anderson, Marie Karen & Prato, Carlo Giacomo, 2021. "Relevance of detailed transfer attributes in large-scale multimodal route choice models for metropolitan public transport passengers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 76-92.
    2. Mepparambath, Rakhi Manohar & Soh, Yong Sheng & Jayaraman, Vasundhara & Tan, Hong En & Ramli, Muhamad Azfar, 2023. "A novel modelling approach of integrated taxi and transit mode and route choice using city-scale emerging mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    3. Chen, Enhui & Stathopoulos, Amanda & Nie, Yu (Marco), 2022. "Transfer station choice in a multimodal transit system: An empirical study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 337-355.
    4. Marie Karen Anderson & Otto Anker Nielsen & Carlo Giacomo Prato, 2017. "Multimodal route choice models of public transport passengers in the Greater Copenhagen Area," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 221-245, September.
    5. Yap, Menno & Cats, Oded, 2021. "Taking the path less travelled: Valuation of denied boarding in crowded public transport systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 1-13.
    6. Mohammad Nurul Hassan & Taha Hossein Rashidi & Neema Nassir, 2021. "Consideration of different travel strategies and choice set sizes in transit path choice modelling," Transportation, Springer, vol. 48(2), pages 723-746, April.
    7. Bouscasse, Hélène & de Lapparent, Matthieu, 2019. "Perceived comfort and values of travel time savings in the Rhône-Alpes Region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 370-387.
    8. Hong, Sung-Pil & Kim, Kyung min & Byeon, Geunyeong & Min, Yun-Hong, 2017. "A method to directly derive taste heterogeneity of travellers’ route choice in public transport from observed routes," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 41-52.
    9. Yu, Chao & Li, Haiying & Xu, Xinyue & Liu, Jun, 2020. "Data-driven approach for solving the route choice problem with traveling backward behavior in congested metro systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    10. Tirachini, Alejandro & Hurtubia, Ricardo & Dekker, Thijs & Daziano, Ricardo A., 2017. "Estimation of crowding discomfort in public transport: Results from Santiago de Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 311-326.
    11. Louis Grange & Felipe González & Ignacio Vargas & Rodrigo Troncoso, 2015. "A Logit Model With Endogenous Explanatory Variables and Network Externalities," Networks and Spatial Economics, Springer, vol. 15(1), pages 89-116, March.
    12. Sung-Pil Hong & Yun-Hong Min & Myoung-Ju Park & Kyung Min Kim & Suk Mun Oh, 2016. "Precise estimation of connections of metro passengers from Smart Card data," Transportation, Springer, vol. 43(5), pages 749-769, September.
    13. Haywood, Luke & Koning, Martin & Prud'homme, Remy, 2018. "The economic cost of subway congestion: Estimates from Paris," Economics of Transportation, Elsevier, vol. 14(C), pages 1-8.
    14. Wu, Laiyun & Kang, Jee Eun & Chung, Younshik & Nikolaev, Alexander, 2021. "Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data," Omega, Elsevier, vol. 101(C).
    15. Marra, Alessio D. & Sun, Linghang & Corman, Francesco, 2022. "The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area," Transport Policy, Elsevier, vol. 116(C), pages 258-268.
    16. Rossetti, Tomás & Daziano, Ricardo A., 2024. "Crowding multipliers on shared transportation in New York City: The effects of COVID-19 and implications for a sustainable future," Transport Policy, Elsevier, vol. 145(C), pages 224-236.
    17. Parbo, Jens & Nielsen, Otto A. & Prato, Carlo G., 2018. "Reducing passengers’ travel time by optimising stopping patterns in a large-scale network: A case-study in the Copenhagen Region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 197-212.
    18. Caterina Malandri & Luca Mantecchini & Filippo Paganelli & Maria Nadia Postorino, 2021. "Public Transport Network Vulnerability and Delay Distribution among Travelers," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    19. Ikki Kim & Hyoung-Chul Kim & Dong-Jeong Seo & Jung In Kim, 2020. "Calibration of a transit route choice model using revealed population data of smartcard in a multimodal transit network," Transportation, Springer, vol. 47(5), pages 2179-2202, October.
    20. Liu, Yang & Feng, Tao & Shi, Zhuangbin & He, Mingwei, 2022. "Understanding the route choice behaviour of metro-bikeshare users," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 460-475.

    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:eee:transa:v:179:y:2024:i:c:s096585642300349x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.