IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v584y2021ics0378437121006245.html
   My bibliography  Save this article

Mining metro commuting mobility patterns using massive smart card data

Author

Listed:
  • Yong, Juan
  • Zheng, Linjiang
  • Mao, Xiaowen
  • Tang, Xi
  • Gao, Ang
  • Liu, Weining

Abstract

With the development of society and economy, the metro has become one of the essential components of the urban transportation system. Commuting passengers prefer the metro due to its punctual, high speeds and uncongested characteristics compared to private cars, taxis, bus, etc., especially in morning and evening rush hour. So, identifying metro commuters and mining its commuting mobility patterns play an essential role in improving service quality, promoting public transit use, and optimizing operational scheduling. We develop a method to mine metro commuting mobility patterns using massive smart card data. Firstly, we extracted individual daily regular OD (origin and destination) based on spatio-temporal similarity measurement from massive smart card data. The information entropy gain algorithm is used to further identify commuters from individual regular OD. Secondly, the station-oriented commute space model is built from space views. Metro stations are divided into employment, residential, and balanced type according to job-housing function pattern. They are divided into high efficiency, general, and low efficiency type according to commute efficiency pattern. Function pattern refers to the proportional relationship between the residence and employment land use around the rail station. Efficiency pattern is a comprehensive index to measure the commute time and distance. Finally, stations are clustered by the K-means method to determine what type they are. The experiment found that metro commuters accounted for 41% of the morning peak traffic using smart card data in Chongqing, China. Three typical job-housing function patterns and three commute efficiency patterns are discovered, respectively, and the characteristics of each are mined.

Suggested Citation

  • Yong, Juan & Zheng, Linjiang & Mao, Xiaowen & Tang, Xi & Gao, Ang & Liu, Weining, 2021. "Mining metro commuting mobility patterns using massive smart card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    • Handle: RePEc:eee:phsmap:v:584:y:2021:i:c:s0378437121006245
    DOI: 10.1016/j.physa.2021.126351
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121006245
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126351?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Zhibin & Wang, Wei & Yang, Chen & Jiang, Guojun, 2013. "Exploring the causal relationship between bicycle choice and trip chain pattern," Transport Policy, Elsevier, vol. 29(C), pages 170-177.
    2. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lingjuan Chen & Yijing Zhao & Zupeng Liu & Xinran Yang, 2022. "Construction of Commuters’ Multi-Mode Choice Model Based on Public Transport Operation Data," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    2. Yang, Hongtai & Ping, An & Wei, Hongmin & Zhai, Guocong, 2023. "Unique in the metro system: The likelihood to re-identify a metro user with limited trajectory points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    3. Elisa Frutos-Bernal & Ángel Martín del Rey & Irene Mariñas-Collado & María Teresa Santos-Martín, 2022. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-17, March.

    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. Chen, Wendong & Cheng, Long & Chen, Xuewu & Chen, Jingxu & Cao, Mengqiu, 2021. "Measuring accessibility to health care services for older bus passengers: A finer spatial resolution," Journal of Transport Geography, Elsevier, vol. 93(C).
    2. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    3. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.
    4. Wang, Jing & Wan, Feng & Dong, Chunjiao & Yin, Chaoying & Chen, Xiaoyu, 2023. "Spatiotemporal effects of built environment factors on varying rail transit station ridership patterns," Journal of Transport Geography, Elsevier, vol. 109(C).
    5. Hainan Huang & Yi Lin & Jiancheng Weng & Jian Rong & Xiaoming Liu, 2018. "Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
    6. Zhou, Yang & Thill, Jean-Claude & Xu, Yang & Fang, Zhixiang, 2021. "Variability in individual home-work activity patterns," Journal of Transport Geography, Elsevier, vol. 90(C).
    7. 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.
    8. 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.
    9. Fulman, Nir & Marinov, Maria & Benenson, Itzhak, 2023. "Investigating occasional travel patterns based on smartcard transactions," Transport Policy, Elsevier, vol. 141(C), pages 152-166.
    10. Ibrahim Abaker Targio Hashem & Raja Sher Afgun Usmani & Mubarak S. Almutairi & Ashraf Osman Ibrahim & Abubakar Zakari & Faiz Alotaibi & Saadat Mehmood Alhashmi & Haruna Chiroma, 2023. "Urban Computing for Sustainable Smart Cities: Recent Advances, Taxonomy, and Open Research Challenges," Sustainability, MDPI, vol. 15(5), pages 1-32, February.
    11. Honghu Sun & Feng Zhen & Yupei Jiang, 2020. "Study on the Characteristics of Urban Residents’ Commuting Behavior and Influencing Factors from the Perspective of Resilience Theory: Theoretical Construction and Empirical Analysis from Nanjing, Chi," IJERPH, MDPI, vol. 17(5), pages 1-17, February.
    12. Sun, Shichao & Duan, Zhengyu, 2019. "Modeling passengers’ loyalty to public transit in a two-dimensional framework: A case study in Xiamen, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 295-309.
    13. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    14. Mepparambath, Rakhi Manohar & Soh, Yong Sheng & Jayaraman, Vasundhara & Tan, Hong En & Ramli, Muhamad Azfar, 2023. "A novel modelling approach of integrated taxi and transit mode and route choice using city-scale emerging mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    15. Shuhong Ma & Yechao Zhou & Zhoulin Yu & Yan Zhang, 2019. "College Students’ Shared Bicycle Use Behavior Based on the NL Model and Factor Analysis," Sustainability, MDPI, vol. 11(17), pages 1-19, August.
    16. Yanyan Chen & Zheng Zhang & Tianwen Liang, 2019. "Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    17. Giménez-Nadal, José Ignacio & Molina, José Alberto & Velilla, Jorge, 2020. "Commuting and self-employment in Western Europe," Journal of Transport Geography, Elsevier, vol. 88(C).
    18. Fangye Du & Jiaoe Wang & Yu Liu & Zihao Zhou & Haitao Jin, 2022. "Equity in Health-Seeking Behavior of Groups Using Different Transportations," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
    19. Sun, Shichao & Xu, Lingyu & Yao, Yukun & Duan, Zhengyu, 2021. "Investigating the determinants to retain spurious-loyalty passengers: A data-fusion based approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 70-83.
    20. Rongrong Hong & Wenming Rao & Dong Zhou & Chengchuan An & Zhenbo Lu & Jingxin Xia, 2020. "Commuting Pattern Recognition Using a Systematic Cluster Framework," Sustainability, MDPI, vol. 12(5), pages 1-20, February.

    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:phsmap:v:584:y:2021:i:c:s0378437121006245. 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: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.