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Spatiotemporal Evolution of Travel Pattern Using Smart Card Data

Author

Listed:
  • Mu Lin

    (School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Zhengdong Huang

    (School of Urban Design, Wuhan University, Wuhan 430072, China
    Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, Shenzhen 518060, China)

  • Tianhong Zhao

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, Shenzhen 518060, China)

  • Ying Zhang

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, Shenzhen 518060, China)

  • Heyi Wei

    (Geodesign Research Centre for Plant, Environment and Humans, Jiangxi Normal University, Nanchang 330022, China)

Abstract

Automated fare collection (AFC) systems can provide tap-in and tap-out records of passengers, allowing us to conduct a comprehensive analysis of spatiotemporal patterns for urban mobility. These temporal and spatial patterns, especially those observed over long periods, provide a better understanding of urban transportation planning and community historical development. In this paper, we explored spatiotemporal evolution of travel patterns using the smart card data of subway traveling from 2011 to 2017 in Shenzhen. To this end, a Gaussian mixture model with expectation–maximization (EM) algorithm clusters the travel patterns according to the frequency characteristics of passengers’ trips. In particular, we proposed the Pareto principle to negotiate diversified evaluation criteria on model parameters. Seven typical travel patterns are obtained using the proposed algorithm. Our findings highlighted that the proportion of each pattern remains relatively stable from 2011 to 2017, but the regular commuting passengers play an increasingly important position in the passenger flow. Additionally, focusing on the busiest commuting passengers, we depicted the spatial variations over years and identified the characters in different periods. Their cross-year usage of smart cards was finally examined to understand the migration of travel patterns over years. With reference to these methods and insights, transportation planners and policymakers can intuitively understand the historical variations of passengers’ travel patterns, which lays the foundation for improving the service of the subway system.

Suggested Citation

  • Mu Lin & Zhengdong Huang & Tianhong Zhao & Ying Zhang & Heyi Wei, 2022. "Spatiotemporal Evolution of Travel Pattern Using Smart Card Data," Sustainability, MDPI, vol. 14(15), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9564-:d:879737
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    References listed on IDEAS

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    1. Sun, Lijun & Axhausen, Kay W., 2016. "Understanding urban mobility patterns with a probabilistic tensor factorization framework," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 511-524.
    2. Jie Huang & David Levinson & Jiaoe Wang & Zi-jia Wang, 2018. "Tracking job and housing dynamics with smartcard data," Working Papers 2018-01, University of Minnesota: Nexus Research Group.
    3. Werner, Carol M. & Brown, Barbara B. & Tribby, Calvin P. & Tharp, Doug & Flick, Kristi & Miller, Harvey J. & Smith, Ken R. & Jensen, Wyatt, 2016. "Evaluating the attractiveness of a new light rail extension: Testing simple change and displacement change hypotheses," Transport Policy, Elsevier, vol. 45(C), pages 15-23.
    4. Ronchetti, Elvezio & Trojani, Fabio, 2001. "Robust inference with GMM estimators," Journal of Econometrics, Elsevier, vol. 101(1), pages 37-69, March.
    5. Chakour, Vincent & Eluru, Naveen, 2016. "Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal," Journal of Transport Geography, Elsevier, vol. 51(C), pages 205-217.
    6. Jie Huang & David Levinson & Jiaoe Wang & Jiangping Zhou & Zi-jia Wang, 2018. "Tracking job and housing dynamics with smartcard data," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(50), pages 12710-12715, December.
    7. Guohong Cheng & Shichao Sun & Linlin Zhou & Guanzhong Wu, 2021. "Using Smart Card Data of Metro Passengers to Unveil the Urban Spatial Structure: A Case Study of Xi’an, China," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, November.
    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. Eun Hak Lee & Inmook Lee & Shin-Hyung Cho & Seung-Young Kho & Dong-Kyu Kim, 2019. "A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
    10. Ahn, Seung C. & Perez, M. Fabricio, 2010. "GMM estimation of the number of latent factors: With application to international stock markets," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 783-802, September.
    11. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    12. Chen Zhong & Michael Batty & Ed Manley & Jiaqiu Wang & Zijia Wang & Feng Chen & Gerhard Schmitt, 2016. "Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    13. Ahn, Seung C. & Perez, M. Fabricio, 2010. "Corrigendum to "GMM estimation of the number of latent factors: With application to international stock markets" [J Empir Financ. 17 (2010) 783-802]," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 1006-1006, December.
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    Cited by:

    1. Jingjing Yan & Zhengdong Huang & Tianhong Zhao & Ying Zhang & Fei Chang, 2023. "Transit Travel Community Detection and Evolutionary Analysis: A Case Study of Shenzhen," Sustainability, MDPI, vol. 15(7), pages 1-17, March.

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