IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v63y2017icp40-47.html

Using metro smart card data to model location choice of after-work activities: An application to Shanghai

Author

Listed:
  • Wang, Yihong
  • Correia, Gonçalo Homem de Almeida
  • de Romph, Erik
  • Timmermans, H.J.P.

Abstract

A location choice model explains how travellers choose their trip destinations especially for those activities which are flexible in space and time. The model is usually estimated using travel survey data; however, little is known about how to use smart card data (SCD) for this purpose in a public transport network. Our study extracted trip information from SCD to model location choice of after-work activities. We newly defined the metrics of travel impedance in this case. Moreover, since socio-demographic information is missing in such anonymous data, we used observable proxy indicators, including commuting distance and the characteristics of one's home and workplace stations, to capture some interpersonal heterogeneity. Such heterogeneity is expected to distinguish the population and better explain the difference of their location choice behaviour. The approach was applied to metro travellers in the city of Shanghai, China. As a result, the model performs well in explaining the choices. Our new metrics of travel impedance to access an after-work activity result in a better model fit than the existing metrics and add additional interpretability to the results. Moreover, the proxy variables distinguishing the population seem to influence the choice behaviour and thus improve the model performance.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jotrge:v:63:y:2017:i:c:p:40-47
    DOI: 10.1016/j.jtrangeo.2017.06.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692317300029
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2017.06.010?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
    ---><---

    References listed on IDEAS

    as
    1. Soora Rasouli & Harry Timmermans, 2014. "Activity-based models of travel demand: promises, progress and prospects," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(1), pages 31-60, March.
    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. Danalet, Antonin & Tinguely, Loïc & Lapparent, Matthieu de & Bierlaire, Michel, 2016. "Location choice with longitudinal WiFi data," Journal of choice modelling, Elsevier, vol. 18(C), pages 1-17.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    5. Schönfelder, Stefan & Axhausen, Kay W., 2003. "Activity spaces: measures of social exclusion?," Transport Policy, Elsevier, vol. 10(4), pages 273-286, October.
    6. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    7. Koppelman, Frank S. & Wen, Chieh-Hua, 2000. "The paired combinatorial logit model: properties, estimation and application," Transportation Research Part B: Methodological, Elsevier, vol. 34(2), pages 75-89, February.
    8. Surinach, Jordi & Romaní, Javier & Royuela, Vicente & Reyes, Monica, 2000. "Urban Systems In The Barcelona Province: A First Step For Estimating Local Economic Activity," ERSA conference papers ersa00p183, European Regional Science Association.
    9. Zhou, Jiangping & Murphy, Enda & Long, Ying, 2014. "Commuting efficiency in the Beijing metropolitan area: an exploration combining smartcard and travel survey data," Journal of Transport Geography, Elsevier, vol. 41(C), pages 175-183.
    10. Jonas De Vos & Tim Schwanen & Veronique Van Acker & Frank Witlox, 2013. "Travel and Subjective Well-Being: A Focus on Findings, Methods and Future Research Needs," Transport Reviews, Taylor & Francis Journals, vol. 33(4), pages 421-442, July.
    11. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    12. Dong, Xiaojing & Ben-Akiva, Moshe E. & Bowman, John L. & Walker, Joan L., 2006. "Moving from trip-based to activity-based measures of accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(2), pages 163-180, February.
    13. Bhat, Chandra R. & Guo, Jessica, 2004. "A mixed spatially correlated logit model: formulation and application to residential choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 38(2), pages 147-168, February.
    14. Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
    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. José-Benito Pérez-López & Margarita Novales & Francisco-Alberto Varela-García & Alfonso Orro, 2020. "Residential Location Econometric Choice Modeling with Irregular Zoning: Common Border Spatial Correlation Metric," Networks and Spatial Economics, Springer, vol. 20(3), pages 785-802, September.
    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. Jie Huang & David Levinson & Jiaoe Wang & Haitao Jin, 2019. "Job-worker spatial dynamics in Beijing: Insights from Smart Card Data," Working Papers 2019-01, University of Minnesota: Nexus Research Group.
    4. Haghani, Milad & Sarvi, Majid & Shahhoseini, Zahra, 2015. "Accommodating taste heterogeneity and desired substitution pattern in exit choices of pedestrian crowd evacuees using a mixed nested logit model," Journal of choice modelling, Elsevier, vol. 16(C), pages 58-68.
    5. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.
    6. Perez-Lopez, Jose-Benito & Novales, Margarita & Orro, Alfonso, 2022. "Spatially correlated nested logit model for spatial location choice," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 1-12.
    7. 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.
    8. Tinessa, Fiore, 2021. "Closed-form random utility models with mixture distributions of random utilities: Exploring finite mixtures of qGEV models," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 262-288.
    9. Antonio Páez & Darren M Scott, 2007. "Social Influence on Travel Behavior: A Simulation Example of the Decision to Telecommute," Environment and Planning A, , vol. 39(3), pages 647-665, March.
    10. Bekhor, Shlomo & Prashker, Joseph N., 2008. "GEV-based destination choice models that account for unobserved similarities among alternatives," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 243-262, March.
    11. Patrick Loa & Khandker Nurul Habib, 2025. "How does public transit serve post-secondary students in Toronto? A utility-based analysis of accessibility by transit for non-mandatory trips," Transportation, Springer, vol. 52(4), pages 1657-1678, August.
    12. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    13. Weiss, Adam & Habib, Khandker Nurul, 2017. "Examining the difference between park and ride and kiss and ride station choices using a spatially weighted error correlation (SWEC) discrete choice model," Journal of Transport Geography, Elsevier, vol. 59(C), pages 111-119.
    14. 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.
    15. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    16. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2022. "Submodularity and local search approaches for maximum capture problems under generalized extreme value models," European Journal of Operational Research, Elsevier, vol. 300(3), pages 953-965.
    17. Tapia, Rodrigo J. & Kourounioti, Ioanna & Thoen, Sebastian & de Bok, Michiel & Tavasszy, Lori, 2023. "A disaggregate model of passenger-freight matching in crowdshipping services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    18. 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.
    19. Chandra Bhat & Ipek Sener, 2009. "A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units," Journal of Geographical Systems, Springer, vol. 11(3), pages 243-272, September.
    20. 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).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jotrge:v:63:y:2017:i:c:p:40-47. 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: https://www.journals.elsevier.com/journal-of-transport-geography .

    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.