IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v636y2024ics037843712400030x.html
   My bibliography  Save this article

Activity-based individual travel regularity exploring with entropy-space K-means clustering using smart card data

Author

Listed:
  • Sun, Li
  • Zhao, Juanjuan
  • Zhang, Jun
  • Zhang, Fan
  • Ye, Kejiang
  • Xu, Chengzhong

Abstract

Travel activities influence individual travel location, time, and frequencies. Understanding individual travel regularity under different activities is crucial for individual mobility prediction and urban facilities planning. Existing studies need further improvement as they have not adequately taken into account the impact of activity factors on individual travel patterns. In this paper, we propose an innovative framework for exploring travel regularity and the correlation with urban region attributes at the individual level. The framework’s novelty and uniqueness lie in its three layers: (i) A trip pre-processing layer for extracting every complete public transit trip and trip purposes (e.g. commuting activity and non-commuting activity) by combining individual’s travel characteristics and public transport network. (ii) An entropy-based travel regularity measurement and K-means based multiple-view clustering layer to assess the extent of individual travel repetition over time for various travel activity and investigate the similarities and differences among users. (iii) A region dependence correlation analysis layer for exploring the correlation between individual travel regularity and regions attributes of two key locations: home and workplace More importantly, by employing the framework, we gained empirical insights based on a large-scale dataset (covering 0.64 million users) collected from public traffic smart cards. For instance, commuters can be categorized into four groups based on the regularity of their commuting activities: Regular Workers (24%), Flex-time Workers (21%), Overtime Workers (35%), and Other Workers (20%). The distribution of these four groups is associated with workplace’s administrative characteristics, with a Pearson correlation of 0.503. In addition, individuals can also be classified into two groups based on their travel regularity of non-commuting activities: Limited Visitors (37%) and Active Explorers (63%). The distribution of these individuals is correlated with the coverage ratio of public transit facility in their home locations, showing a Pearson correlation of 0.604.

Suggested Citation

  • Sun, Li & Zhao, Juanjuan & Zhang, Jun & Zhang, Fan & Ye, Kejiang & Xu, Chengzhong, 2024. "Activity-based individual travel regularity exploring with entropy-space K-means clustering using smart card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    • Handle: RePEc:eee:phsmap:v:636:y:2024:i:c:s037843712400030x
    DOI: 10.1016/j.physa.2024.129522
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712400030X
    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.2024.129522?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. Kang, Chaogui & Ma, Xiujun & Tong, Daoqin & Liu, Yu, 2012. "Intra-urban human mobility patterns: An urban morphology perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1702-1717.
    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. F. Benjamin Zhan & Charles E. Noon, 1998. "Shortest Path Algorithms: An Evaluation Using Real Road Networks," Transportation Science, INFORMS, vol. 32(1), pages 65-73, February.
    4. Hou, Qinzhong & Leng, Junqiang & Ma, Guosheng & Liu, Weiyi & Cheng, Yuxing, 2019. "An adaptive hybrid model for short-term urban traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    5. Yong, Nuo & Ni, Shunjiang & Shen, Shifei & Chen, Peng & Ji, Xuewei, 2018. "Uncovering stable and occasional human mobility patterns: A case study of the Beijing subway," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 28-38.
    6. Næss, Petter & Peters, Sebastian & Stefansdottir, Harpa & Strand, Arvid, 2018. "Causality, not just correlation: Residential location, transport rationales and travel behavior across metropolitan contexts," Journal of Transport Geography, Elsevier, vol. 69(C), pages 181-195.
    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. Guerra, Erick & Caudillo, Camilo & Monkkonen, Paavo & Montejano, Jorge, 2018. "Urban form, transit supply, and travel behavior in Latin America: Evidence from Mexico's 100 largest urban areas," Transport Policy, Elsevier, vol. 69(C), pages 98-105.
    9. 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).
    10. Huang, Zhiren & Wang, Pu & Zhang, Fan & Gao, Jianxi & Schich, Maximilian, 2018. "A mobility network approach to identify and anticipate large crowd gatherings," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 147-170.
    11. Qingru Zou & Xiangming Yao & Peng Zhao & Heng Wei & Hui Ren, 2018. "Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway," Transportation, Springer, vol. 45(3), pages 919-944, May.
    12. Kitamura, Ryuichi & Yamamoto, Toshiyuki & Susilo, Yusak O. & Axhausen, Kay W., 2006. "How routine is a routine? An analysis of the day-to-day variability in prism vertex location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(3), pages 259-279, March.
    13. Gutiérrez, Aaron & Domènech, Antoni & Zaragozí, Benito & Miravet, Daniel, 2020. "Profiling tourists' use of public transport through smart travel card data," Journal of Transport Geography, Elsevier, vol. 88(C).
    14. Guo, Bao & Li, Minglun & Zhou, Mengnan & Zhang, Fan & Wang, Pu, 2023. "A new anomalous travel demand prediction method combining Markov model and complex network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    15. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    16. Mouratidis, Kostas & Ettema, Dick & Næss, Petter, 2019. "Urban form, travel behavior, and travel satisfaction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 306-320.
    17. D. Brockmann & L. Hufnagel & T. Geisel, 2006. "The scaling laws of human travel," Nature, Nature, vol. 439(7075), pages 462-465, January.
    18. Li, Ruqi & Song, Yurong & Wang, Haiyan & Jiang, Guo-Ping & Xiao, Min, 2023. "Reactive–diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    19. Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    20. Arbex, Renato & Cunha, Claudio B., 2020. "Estimating the influence of crowding and travel time variability on accessibility to jobs in a large public transport network using smart card big data," Journal of Transport Geography, Elsevier, vol. 85(C).
    Full references (including those not matched with items on IDEAS)

    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. Shanshan Wan & Zhuo Chen & Cheng Lyu & Ruofan Li & Yuntao Yue & Ying Liu, 2022. "Research on disaster information dissemination based on social sensor networks," International Journal of Distributed Sensor Networks, , vol. 18(3), pages 15501329221, March.
    2. He, Zhengbing, 2020. "Spatial-temporal fractal of urban agglomeration travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    4. 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.
    5. 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).
    6. Xiping Yang & Zhiyuan Zhao & Shiwei Lu, 2016. "Exploring Spatial-Temporal Patterns of Urban Human Mobility Hotspots," Sustainability, MDPI, vol. 8(7), pages 1-18, July.
    7. Maxime Lenormand & Miguel Picornell & Oliva G Cantú-Ros & Antònia Tugores & Thomas Louail & Ricardo Herranz & Marc Barthelemy & Enrique Frías-Martínez & José J Ramasco, 2014. "Cross-Checking Different Sources of Mobility Information," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
    8. Huang, Feihu & Qiao, Shaojie & Peng, Jian & Guo, Bing & Xiong, Xi & Han, Nan, 2019. "A movement model for air passengers based on trip purpose," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 798-808.
    9. Pieroni, Caio & Giannotti, Mariana & Alves, Bianca B. & Arbex, Renato, 2021. "Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city," Journal of Transport Geography, Elsevier, vol. 96(C).
    10. Chaogui Kang & Yu Liu & Diansheng Guo & Kun Qin, 2015. "A Generalized Radiation Model for Human Mobility: Spatial Scale, Searching Direction and Trip Constraint," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-11, November.
    11. Li, Ze-Tao & Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2023. "Exploring the topological characteristics of urban trip networks based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    12. Cai, Hua & Zhan, Xiaowei & Zhu, Ji & Jia, Xiaoping & Chiu, Anthony S.F. & Xu, Ming, 2016. "Understanding taxi travel patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 590-597.
    13. Toru Nakamura & Toru Takumi & Atsuko Takano & Fumiyuki Hatanaka & Yoshiharu Yamamoto, 2013. "Characterization and Modeling of Intermittent Locomotor Dynamics in Clock Gene-Deficient Mice," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-8, March.
    14. Chen, Roger B., 2018. "Models of count with endogenous choices," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 862-875.
    15. Wang, Wenjun & Pan, Lin & Yuan, Ning & Zhang, Sen & Liu, Dong, 2015. "A comparative analysis of intra-city human mobility by taxi," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 134-147.
    16. Barmak, D.H. & Dorso, C.O. & Otero, M., 2016. "Modelling dengue epidemic spreading with human mobility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 129-140.
    17. Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2022. "Revealing mobility pattern of taxi movements with its travel trajectory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    18. Christensen, Claire & Albert, István & Grenfell, Bryan & Albert, Réka, 2010. "Disease dynamics in a dynamic social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(13), pages 2663-2674.
    19. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    20. Li, Jun & Fu, Siyao & He, Haibo & Jia, Hongfei & Li, Yanzhong & Guo, Yi, 2015. "Simulating large-scale pedestrian movement using CA and event driven model: Methodology and case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 304-321.

    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:636:y:2024:i:c:s037843712400030x. 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.