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Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore

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  • Yi Zhu

    (Shanghai University of Finance and Economics)

Abstract

Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer transit riders’ activities from their smart card transaction records. Using Singapore as an example, activity type classification models were built using household travel survey and a rich set of urban built environment measures to reveal the spatial and temporal correspondences that indicate the activity participation of transit riders. The calibrated model is then applied to the transit smart card dataset to extract the embedded activity information. The proposed approach enables to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new ‘‘big data’’ such as transit smart card data and “small data” such as traditional travel surveys to create better insights of urban travel demand and activity dynamics.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:transp:v:47:y:2020:i:6:d:10.1007_s11116-018-9881-8
    DOI: 10.1007/s11116-018-9881-8
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    References listed on IDEAS

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    1. 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.
    2. Mi Diao & Yi Zhu & Joseph Ferreira Jr & Carlo Ratti, 2016. "Inferring individual daily activities from mobile phone traces: A Boston example," Environment and Planning B, , vol. 43(5), pages 920-940, September.
    3. Deakin, Elizabeth & Kim, Songju, 2001. "Transportation Technologies: Implications for Planning," University of California Transportation Center, Working Papers qt9gt0f9d2, University of California Transportation Center.
    4. 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.
    5. 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.
    6. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    7. 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.
    8. Deakin, Elizabeth & Kim, Songju, 2001. "Transportation Technologies: Implications for Planning," University of California Transportation Center, Working Papers qt0bg7n68t, University of California Transportation Center.
    9. Martin Herold & Joseph Scepan & Keith C Clarke, 2002. "The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses," Environment and Planning A, , vol. 34(8), pages 1443-1458, August.
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    Cited by:

    1. Jin, Meihan & Wang, Menghan & Gong, Yongxi & Liu, Yu, 2022. "Spatio-temporally constrained origin–destination inferring using public transit fare card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    2. Yi Zhu, 2022. "Inference of activity patterns from urban sensing data using conditional random fields," Environment and Planning B, , vol. 49(2), pages 549-565, February.

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