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Evaluation and determinants of metro users' regularity: Insights from transit one-card data

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  • Ma, Xinwei
  • Tian, Xiaolin
  • Jin, Zejin
  • Cui, Hongjun
  • Ji, Yanjie
  • Cheng, Long

Abstract

Regularity is typically defined based on the repetitive travel behavior of individuals, referring to how often travelers would utilize a specific service within a given spatio-temporal context. However, previous research on metro users' regularity primarily utilized basic metric, for example metro trip frequency, to measure regularity. What's more, metro smart card data typically encompasses time, spatial features, and card type information, lacking individual attributes such as age, gender, and type of residence, which limits in-depth analysis correlating individual attributes with travel behavior. The study obtained transit one-card data from Nanjing, China, which enabled us to extract metro user's travel and individual information. Thus, the entropy rate methodology was employed to measure metro users' regularity, while machine learning techniques were used to analyze non-linear effects of built environment, travel-related, and individual attributes on regularity. Results indicate that the built environment, travel-related, and individual attributes account for 66.66%, 33.31%, and 0.03% of the total relative importance, respectively. Two most influential variables impacting regularity, namely entertainment POIs at the origin level (17.77%) and weekdays (17.51%), belong to the built environment and travel-related attributes, respectively. In terms of individual attributes, age exhibits a greater impact on regularity compared to gender and type of residence, manifested in the variation of regularity among different age groups. This finding can assist metro policymakers in understanding metro users' travel behavior, aiming to enhance operational efficiency and optimize the user experience.

Suggested Citation

  • Ma, Xinwei & Tian, Xiaolin & Jin, Zejin & Cui, Hongjun & Ji, Yanjie & Cheng, Long, 2024. "Evaluation and determinants of metro users' regularity: Insights from transit one-card data," Journal of Transport Geography, Elsevier, vol. 118(C).
    • Handle: RePEc:eee:jotrge:v:118:y:2024:i:c:s096669232400142x
    DOI: 10.1016/j.jtrangeo.2024.103933
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    as
    1. Eric I. Pas, 1983. "A Flexible and Integrated Methodology for Analytical Classification of Daily Travel-Activity Behavior," Transportation Science, INFORMS, vol. 17(4), pages 405-429, November.
    2. Kim, Kyoungok, 2018. "Exploring the difference between ridership patterns of subway and taxi: Case study in Seoul," Journal of Transport Geography, Elsevier, vol. 66(C), pages 213-223.
    3. Wang, Bobin & Shao, Chunfu & Li, Juan & Weng, Jinxian & Ji, Xun, 2015. "Holiday travel behavior analysis and empirical study under integrated multimodal travel information service," Transport Policy, Elsevier, vol. 39(C), pages 21-36.
    4. Hongyun Si & Jiangang Shi & Wenwen Hua & Long Cheng & Jonas De Vos & Wenxiang Li, 2023. "What influences people to choose ridesharing? An overview of the literature," Transport Reviews, Taylor & Francis Journals, vol. 43(6), pages 1211-1236, November.
    5. Sun, Daniel (Jian) & Guan, Shituo, 2016. "Measuring vulnerability of urban metro network from line operation perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 348-359.
    6. Jinjun Tang & Xiaolu Wang & Fang Zong & Zheng Hu, 2020. "Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    7. 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.
    8. Iacono, Michael & Krizek, Kevin J. & El-Geneidy, Ahmed, 2010. "Measuring non-motorized accessibility: issues, alternatives, and execution," Journal of Transport Geography, Elsevier, vol. 18(1), pages 133-140.
    9. Yuanyuan Guo & Linchuan Yang & Wenke Huang & Yi Guo, 2020. "Traffic Safety Perception, Attitude, and Feeder Mode Choice of Metro Commute: Evidence from Shenzhen," IJERPH, MDPI, vol. 17(24), pages 1-20, December.
    10. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    11. Gan, Zuoxian & Yang, Min & Zeng, Qingcheng & Timmermans, Harry J.P., 2021. "Associations between built environment, perceived walkability/bikeability and metro transfer patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 171-187.
    12. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
    13. 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.
    14. Deeksha Tayal & Aasha Kapur Mehta, 2021. "Working Women, Delhi Metro and Covid-19: A Case Study in Delhi-NCR," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 64(2), pages 389-413, June.
    15. Robert Schlich & Kay Axhausen, 2003. "Habitual travel behaviour: Evidence from a six-week travel diary," Transportation, Springer, vol. 30(1), pages 13-36, February.
    16. Chuan Ding & Donggen Wang & Xiaolei Ma & Haiying Li, 2016. "Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees," Sustainability, MDPI, vol. 8(11), pages 1-16, October.
    17. Charles Raux & Tai-Yu Ma & Eric Cornelis, 2016. "Variability in daily activity-travel patterns: the case of a one-week travel diary," Post-Print halshs-01389479, HAL.
    18. Veronique Acker & Frank Witlox, 2011. "Commuting trips within tours: how is commuting related to land use?," Transportation, Springer, vol. 38(3), pages 465-486, May.
    19. Vale, David S. & Viana, Cláudia M. & Pereira, Mauro, 2018. "The extended node-place model at the local scale: Evaluating the integration of land use and transport for Lisbon's subway network," Journal of Transport Geography, Elsevier, vol. 69(C), pages 282-293.
    20. Lei, Da & Cheng, Long & Wang, Pengfei & Chen, Xuewu & Zhang, Lin, 2024. "Identifying service bottlenecks in public bikesharing flow networks," Journal of Transport Geography, Elsevier, vol. 116(C).
    21. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    22. Aya Hasan AlKhereibi & Tadesse G. Wakjira & Murat Kucukvar & Nuri C. Onat, 2023. "Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    23. Oscar Egu & Patrick Bonnel, 2020. "Investigating day-to-day variability of transit usage on a multimonth scale with smart card data. A case study in Lyon," Post-Print halshs-03148937, HAL.
    24. Yusak Susilo & Kay Axhausen, 2014. "Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index," Transportation, Springer, vol. 41(5), pages 995-1011, September.
    25. Ed Manley & Chen Zhong & Michael Batty, 2018. "Spatiotemporal variation in travel regularity through transit user profiling," Transportation, Springer, vol. 45(3), pages 703-732, May.
    26. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu (Jason) & Yang, Jiawen, 2023. "Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city," Journal of Transport Geography, Elsevier, vol. 110(C).
    27. Fuquan Pan & Shuai Cheng & Haitao Pan & Shiwei Li & Lixia Zhang & Jinshun Yang, 2024. "Satisfaction Analysis of Urban Rail Transit Based on the Personal Characteristics of Passengers," Sustainability, MDPI, vol. 16(9), pages 1-19, April.
    28. Yang Liu & Yanjie Ji & Zhuangbin Shi & Liangpeng Gao, 2018. "The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    29. Cheng, Long & Cai, Xinmei & Liu, Zhuo & Huang, Zhiren & Chen, Wendong & Witlox, Frank, 2024. "Characterising travel behaviour patterns of transport hub station area users using mobile phone data," Journal of Transport Geography, Elsevier, vol. 116(C).
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