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Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data

Author

Listed:
  • Pengfei Lin

    (The Key Laboratory of Transportation Engineering, Beijing University of Technology, No. 100 Ping Le Yuan, Chaoyang District, Beijing 100124, China)

  • Jiancheng Weng

    (The Key Laboratory of Transportation Engineering, Beijing University of Technology, No. 100 Ping Le Yuan, Chaoyang District, Beijing 100124, China)

  • Dimitrios Alivanistos

    (Elsevier B.V., Radarweg 29a, 1043 NX Amsterdam, The Netherlands)

  • Siyong Ma

    (The Key Laboratory of Transportation Engineering, Beijing University of Technology, No. 100 Ping Le Yuan, Chaoyang District, Beijing 100124, China)

  • Baocai Yin

    (The Key Laboratory of Transportation Engineering, Beijing University of Technology, No. 100 Ping Le Yuan, Chaoyang District, Beijing 100124, China)

Abstract

Understanding commuting patterns could provide effective support for the planning and operation of public transport systems. One-month smart card data and travel behavior survey data in Beijing were integrated to complement the socioeconomic attributes of cardholders. The light gradient boosting machine (LightGBM) was introduced to identify the commuting patterns considering the spatiotemporal regularity of travel behavior. Commuters were further divided into fine-grained clusters according to their departure time using the latent Dirichlet allocation model. To enhance the interpretation of the behavior patterns in each cluster, we investigated the relationship between the socioeconomic characteristics of the residence locations and commuter cluster distributions. Approximately 3.1 million cardholders were identified as commuters, accounting for 67.39% of daily passenger volume. Their commuting routes indicated the existence of job–house imbalance and excess commuting in Beijing. We further segmented commuters into six clusters with different temporal patterns, including two-peak, staggered shifts, flexible departure time, and single-peak. The residences of commuters are mainly concentrated in the low housing price and high or medium population density areas; subway facilities will promote people to commute using public transport. This study will help stakeholders optimize the public transport networks, scheduling scheme, and policy accordingly, thus ameliorating commuting within cities.

Suggested Citation

  • Pengfei Lin & Jiancheng Weng & Dimitrios Alivanistos & Siyong Ma & Baocai Yin, 2020. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:5010-:d:373532
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    References listed on IDEAS

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    Cited by:

    1. Cardell-Oliver, Rachel & Olaru, Doina, 2022. "CIAM: A data-driven approach for classifying long-term engagement of public transport riders at multiple temporal scales," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 321-336.
    2. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.
    3. Shi, Linchang & Yang, Jiayu & Lee, Jaeyoung Jay & Bai, Jun & Ryu, Ingon & Choi, Keechoo, 2024. "Spatial-temporal identification of commuters using trip chain data from non-motorized mode incentive program and public transportation," Journal of Transport Geography, Elsevier, vol. 117(C).

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