IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v9y2017i1d10.1007_s12469-016-0145-8.html
   My bibliography  Save this article

Journey-based characterization of multi-modal public transportation networks

Author

Listed:
  • Cecilia Viggiano

    (Massachusetts Institute of Technology)

  • Haris N. Koutsopoulos

    (Northeastern University, 403 Snell Engineering Center)

  • Nigel H. M. Wilson

    (Massachusetts Institute of Technology)

  • John Attanucci

    (Massachusetts Institute of Technology)

Abstract

Planners must understand how public transportation systems are used in order to make strategic decisions. Smart card transaction data provides vast, detailed records of network usage. Combined with other automatically collected data sources, established inference methodologies can convert smart card transactions into complete linked journeys made by individuals within the public transport network. However, for large, multi-modal public transport networks it can be challenging to summarize the journey records meaningfully. This paper develops a method for categorizing origin–destination (OD) pairs by public transport mode or combination of used modes. By aggregating across OD pairs, this categorization scheme summarizes the multi-modal aspects of public transport network usage. The methodology can also be applied to subsets of data filtered by time of day or geography. The categorization results can inform performance analysis of OD pairs, allowing planners to make comparisons between pairs served by different combinations of modes. London Oyster card data is analyzed to illustrate how the OD pair categorization can characterize a network, allowing planners to quickly assess the roles of different modes, and perform OD pair analysis in a multi-modal public transport network.

Suggested Citation

  • Cecilia Viggiano & Haris N. Koutsopoulos & Nigel H. M. Wilson & John Attanucci, 2017. "Journey-based characterization of multi-modal public transportation networks," Public Transport, Springer, vol. 9(1), pages 437-461, July.
  • Handle: RePEc:spr:pubtra:v:9:y:2017:i:1:d:10.1007_s12469-016-0145-8
    DOI: 10.1007/s12469-016-0145-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-016-0145-8
    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/s12469-016-0145-8?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. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    2. 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.
    3. Sybil Derrible & Christopher Kennedy, 2010. "Characterizing metro networks: state, form, and structure," Transportation, Springer, vol. 37(2), pages 275-297, March.
    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. Li He & Martin Trépanier & Bruno Agard, 2021. "Space–time classification of public transit smart card users’ activity locations from smart card data," Public Transport, Springer, vol. 13(3), pages 579-595, October.
    3. Naima Islam & Md Abu Sufian Talukder & Alex Hainen & Travis Atkison, 2020. "Characterizing co-modality in urban transit systems from a passengers’ perspective," Public Transport, Springer, vol. 12(2), pages 405-430, June.

    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. 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.
    2. De Zhao & Wei Wang & Amber Woodburn & Megan S. Ryerson, 2017. "Isolating high-priority metro and feeder bus transfers using smart card data," Transportation, Springer, vol. 44(6), pages 1535-1554, November.
    3. Amarin Siripanich & Taha Hossein Rashidi & Emily Moylan, 2019. "Interaction of Public Transport Accessibility and Residential Property Values Using Smart Card Data," Sustainability, MDPI, vol. 11(9), pages 1-24, May.
    4. Bernal, Margarita & Welch, Eric W. & Sriraj, P.S., 2016. "The effect of slow zones on ridership: An analysis of the Chicago Transit Authority “El” Blue Line," Transportation Research Part A: Policy and Practice, Elsevier, vol. 87(C), pages 11-21.
    5. Amaya, Margarita & Cruzat, Ramón & Munizaga, Marcela A., 2018. "Estimating the residence zone of frequent public transport users to make travel pattern and time use analysis," Journal of Transport Geography, Elsevier, vol. 66(C), pages 330-339.
    6. Zijia Wang & Hao Tang & Wenjuan Wang & Yang Xi, 2020. "The Pattern of Non-Roundtrip Travel on Urban Rail and Its Application in Transit Improvement," Sustainability, MDPI, vol. 12(9), pages 1-16, April.
    7. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    8. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    9. Benito Zaragozí & Sergio Trilles & Aaron Gutiérrez & Daniel Miravet, 2021. "Development of a Common Framework for Analysing Public Transport Smart Card Data," Energies, MDPI, vol. 14(19), pages 1-22, September.
    10. 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.
    11. Ying Song & Yingling Fan & Xin Li & Yanjie Ji, 2018. "Multidimensional visualization of transit smartcard data using space–time plots and data cubes," Transportation, Springer, vol. 45(2), pages 311-333, March.
    12. Páez, Antonio & Trépanier, Martin & Morency, Catherine, 2011. "Geodemographic analysis and the identification of potential business partnerships enabled by transit smart cards," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(7), pages 640-652, August.
    13. Agarwal, Sumit & Diao, Mi & Keppo, Jussi & Sing, Tien Foo, 2020. "Preferences of public transit commuters: Evidence from smart card data in Singapore," Journal of Urban Economics, Elsevier, vol. 120(C).
    14. 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.
    15. Cao, Zhejing & Zhang, Xiaohu & Chua, Kelman & Yu, Honghai & Zhao, Jinhua, 2021. "E-scooter sharing to serve short-distance transit trips: A Singapore case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 177-196.
    16. Cats, Oded & Jenelius, Erik, 2015. "Planning for the unexpected: The value of reserve capacity for public transport network robustness," Transportation Research Part A: Policy and Practice, Elsevier, vol. 81(C), pages 47-61.
    17. Kevin Credit & Zander Arnao, 2023. "A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data," Environment and Planning B, , vol. 50(3), pages 709-722, March.
    18. Ali Enes Dingil & Federico Rupi & Domokos Esztergár-Kiss, 2021. "An Integrative Review of Socio-Technical Factors Influencing Travel Decision-Making and Urban Transport Performance," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    19. Hong, Liu & Ye, Bowen & Yan, Han & Zhang, Hui & Ouyang, Min & (Sean) He, Xiaozheng, 2019. "Spatiotemporal vulnerability analysis of railway systems with heterogeneous train flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 725-744.
    20. Apanasevic, Tatjana & Rudmark, Daniel, 2021. "Crowdsourcing and Public Transportation: Barriers and Opportunities," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238005, International Telecommunications Society (ITS).

    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:9:y:2017:i:1:d:10.1007_s12469-016-0145-8. 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.