IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v47y2020i5d10.1007_s11116-019-10008-8.html
   My bibliography  Save this article

Calibration of a transit route choice model using revealed population data of smartcard in a multimodal transit network

Author

Listed:
  • Ikki Kim

    (Hanyang University)

  • Hyoung-Chul Kim

    (ChungNam Institute)

  • Dong-Jeong Seo

    (Korea Transportation Safety Authority)

  • Jung In Kim

    (Hanyang University)

Abstract

One of the major objectives of this study is to provide more realistic and accurate results related to transit passenger’s route choice behavior by using population data of revealed preference from smartcard transaction records. The smartcard data of the Seoul city provides both boarding and alighting location and time, which can make possible to trace each passenger’s actually used path trajectory with close to 100% market penetration of smartcard usage. This study built an abstract transit network with representative nodes by aggregating all near-by bus stops within walkable distance and with abstract paths by aggregating lines for a specific OD pair that run the same trajectory links by same transit modes. This complex and huge-scale transit network allowed to analyze the route choice behavior of transit passengers in a multimodal transit system that could not be found from the data of relatively small-size cities. This study selected OD pairs which had two or more alternative paths in order to analyze choice behavior requiring a plural alternative choice set. The number of the selected OD pairs are 124,393 pairs that are 33.9% of whole OD pairs that has two or more trip records. The calibration result showed that it is good statistically and logically to include the six explanatory variables in the utility function of the multinomial Logit model. Those are in-vehicle travel time, out-of-vehicle travel time, transfer penalty index, travel time reliability measure, and path circuity index.

Suggested Citation

  • 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.
  • Handle: RePEc:kap:transp:v:47:y:2020:i:5:d:10.1007_s11116-019-10008-8
    DOI: 10.1007/s11116-019-10008-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-019-10008-8
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11116-019-10008-8?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. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
    2. 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.
    3. 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.
    4. 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.
    5. Jaap Vreeswijk & Tom Thomas & Eric Berkum & Bart Arem, 2014. "Perception bias in route choice," Transportation, Springer, vol. 41(6), pages 1305-1321, November.
    6. Piet Bovy & Sascha Hoogendoorn-Lanser, 2005. "Modelling route choice behaviour in multi-modal transport networks," Transportation, Springer, vol. 32(4), pages 341-368, July.
    7. Tirachini, Alejandro & Sun, Lijun & Erath, Alexander & Chakirov, Artem, 2016. "Valuation of sitting and standing in metro trains using revealed preferences," Transport Policy, Elsevier, vol. 47(C), pages 94-104.
    8. Yap, M.D. & Nijënstein, S. & van Oort, N., 2018. "Improving predictions of public transport usage during disturbances based on smart card data," Transport Policy, Elsevier, vol. 61(C), pages 84-95.
    9. de Palma, André & Picard, Nathalie, 2005. "Route choice decision under travel time uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(4), pages 295-324, May.
    10. Guo, Zhan, 2011. "Mind the map! The impact of transit maps on path choice in public transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(7), pages 625-639, August.
    11. Carlo Giacomo Prato & Shlomo Bekhor & Cristina Pronello, 2012. "Latent variables and route choice behavior," Post-Print halshs-00733464, HAL.
    12. Carlo Prato & Shlomo Bekhor & Cristina Pronello, 2012. "Latent variables and route choice behavior," Transportation, Springer, vol. 39(2), pages 299-319, March.
    13. Qingru Zou & Xiangming Yao & Peng Zhao & Heng Wei & Hui Ren, 2018. "Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway," Transportation, Springer, vol. 45(3), pages 919-944, May.
    14. Hironori Kato & Yuichiro Kaneko & Masashi Inoue, 2010. "Comparative analysis of transit assignment: evidence from urban railway system in the Tokyo Metropolitan Area," Transportation, Springer, vol. 37(5), pages 775-799, September.
    15. Takahiko Kusakabe & Takamasa Iryo & Yasuo Asakura, 2010. "Estimation method for railway passengers’ train choice behavior with smart card transaction data," Transportation, Springer, vol. 37(5), pages 731-749, September.
    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. 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.
    2. 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).
    3. 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).
    4. 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.

    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. Junghan Baek & Keemin Sohn, 2016. "An investigation into passenger preference for express trains during peak hours," Transportation, Springer, vol. 43(4), pages 623-641, July.
    2. 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.
    3. Weckström, Christoffer & Mladenović, Miloš N. & Kujala, Rainer & Saramäki, Jari, 2021. "Navigability assessment of large-scale redesigns in nine public transport networks: Open timetable data approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 212-229.
    4. 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.
    5. Yi Zhu, 2020. "Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore," Transportation, Springer, vol. 47(6), pages 2703-2730, December.
    6. Dalumpines, Ron & Scott, Darren M., 2017. "Determinants of route choice behavior: A comparison of shop versus work trips using the Potential Path Area - Gateway (PPAG) algorithm and Path-Size Logit," Journal of Transport Geography, Elsevier, vol. 59(C), pages 59-68.
    7. 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.
    8. 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.
    9. Frappier, Alexis & Morency, Catherine & Trépanier, Martin, 2018. "Measuring the quality and diversity of transit alternatives," Transport Policy, Elsevier, vol. 61(C), pages 51-59.
    10. Bartosz Bursa & Markus Mailer & Kay W. Axhausen, 2022. "Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work," Transportation, Springer, vol. 49(5), pages 1465-1516, October.
    11. Ding Luo & Oded Cats & Hans Lint, 2020. "Can passenger flow distribution be estimated solely based on network properties in public transport systems?," Transportation, Springer, vol. 47(6), pages 2757-2776, December.
    12. Pieroni, Caio & Giannotti, Mariana & Alves, Bianca B. & Arbex, Renato, 2021. "Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city," Journal of Transport Geography, Elsevier, vol. 96(C).
    13. Gustavo García-Melero & Rubén Sainz-González & Pablo Coto-Millán & Alejandra Valencia-Vásquez, 2021. "Sustainable Mobility Policy Analysis Using Hybrid Choice Models: Is It the Right Choice?," Sustainability, MDPI, vol. 13(5), pages 1-16, March.
    14. Wei Zhu & Wei-li Fan & Amr M. Wahaballa & Jin Wei, 2020. "Calibrating travel time thresholds with cluster analysis and AFC data for passenger reasonable route generation on an urban rail transit network," Transportation, Springer, vol. 47(6), pages 3069-3090, December.
    15. Li, Hao & Gao, Kun & Tu, Huizhao, 2017. "Variations in mode-specific valuations of travel time reliability and in-vehicle crowding: Implications for demand estimation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 250-263.
    16. Kim, Seheon & Rasouli, Soora, 2022. "The influence of latent lifestyle on acceptance of Mobility-as-a-Service (MaaS): A hierarchical latent variable and latent class approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 304-319.
    17. 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.
    18. Chorus, Caspar G. & Kroesen, Maarten, 2014. "On the (im-)possibility of deriving transport policy implications from hybrid choice models," Transport Policy, Elsevier, vol. 36(C), pages 217-222.
    19. Daniel A Rodriguez & Jennifer Rogers, 2014. "Can Housing and Accessibility Information Influence Residential Location Choice and Travel Behavior? An Experimental Study," Environment and Planning B, , vol. 41(3), pages 534-550, June.
    20. 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.

    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:kap:transp:v:47:y:2020:i:5:d:10.1007_s11116-019-10008-8. 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.