IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i4p1475-d321382.html
   My bibliography  Save this article

Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China

Author

Listed:
  • Jinjun Tang

    (Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Xiaolu Wang

    (Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Fang Zong

    (College of Transportation, Jilin University, Changchun 130012, China)

  • Zheng Hu

    (School of Intelligent transportation, Hunan Communication Engineering Polytechnic, Changsha 410132, China)

Abstract

Individual mobility patterns are an important factor in urban traffic planning and traffic flow forecasting. How to understand the spatio-temporal distribution of passengers deeply and accurately, so as to provide theoretical support for the planning and operation of the metro network, is an urgent issue of wide concern. In this paper, we applied NCP decomposition to uncover the characteristics of travel patterns from temporal and spatial dimensions in the metro network of Shenzhen City. Utilizing matrix factorization and correlation analysis, we extracted several stable components from the collective mobility and find that the departure and arrival mobility patterns have different characteristics in both the temporal and spatial dimension. According to the point of interest (POI) data in the Shenzhen City, the function attributes of the station are identified and then we found that the spatial distribution characteristics of different patterns are different. We explored the distribution of travel time classified according to the spatio-temporal characteristics of stable patterns. The proposed method can decompose stable travel patterns from the collective mobility and the results in this study can help us to better understand different mobility patterns in both spatial and temporal dimensions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1475-:d:321382
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/4/1475/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/4/1475/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Du, Zhouyang & Tang, Jinjun & Qi, Yong & Wang, Yiwei & Han, Chunyang & Yang, Yifan, 2020. "Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    2. 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.
    3. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    4. Chengbin Peng & Xiaogang Jin & Ka-Chun Wong & Meixia Shi & Pietro Liò, 2012. "Collective Human Mobility Pattern from Taxi Trips in Urban Area," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-8, April.
    5. 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.
    6. 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.
    7. Yanshuo Sun & Jungang Shi & Paul M. Schonfeld, 2016. "Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro," Public Transport, Springer, vol. 8(3), pages 341-363, December.
    8. 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. Shi, Shuyang & Wang, Lin & Wang, Xiaofan, 2022. "Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    2. Tang, Jinjun & Li, Zhitao & Gao, Fan & Zong, Fang, 2021. "Identifying critical metro stations in multiplex network based on D–S evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(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.
    4. 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.

    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. Shi, Shuyang & Wang, Lin & Wang, Xiaofan, 2022. "Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    2. 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).
    3. 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).
    4. 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.
    5. 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.
    6. Zhanhong Cheng & Martin Trépanier & Lijun Sun, 2021. "Probabilistic model for destination inference and travel pattern mining from smart card data," Transportation, Springer, vol. 48(4), pages 2035-2053, August.
    7. Yong Gao & Jiajun Liu & Yan Xu & Lan Mu & Yu Liu, 2019. "A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips," Sustainability, MDPI, vol. 11(15), pages 1-22, August.
    8. Deng, Yue & Wang, Jiaxin & Gao, Chao & Li, Xianghua & Wang, Zhen & Li, Xuelong, 2021. "Assessing temporal–spatial characteristics of urban travel behaviors from multiday smart-card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
    9. 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.
    10. Peikun Li & Chaoqun Ma & Jing Ning & Yun Wang & Caihua Zhu, 2019. "Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    11. Paulsen, Mads & Rasmussen, Thomas Kjær & Nielsen, Otto Anker, 2021. "Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 155-182.
    12. Jihui Ma & Cuiying Song & Avishai (Avi) Ceder & Tao Liu & Wei Guan, 2017. "Fairness in optimizing bus-crew scheduling process," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-19, November.
    13. 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).
    14. Duan, Zhengyu & Zhao, Haoran & Li, Zhenming, 2023. "Non-linear effects of built environment and socio-demographics on activity space," Journal of Transport Geography, Elsevier, vol. 111(C).
    15. 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).
    16. Christian Martin Mützel & Joachim Scheiner, 2022. "Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data," Public Transport, Springer, vol. 14(2), pages 343-366, June.
    17. 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.
    18. Tong, Zhaomin & Zhang, Ziyi & An, Rui & Liu, Yaolin & Chen, Huiting & Xu, Jiwei & Fu, Shihang, 2024. "Detecting anomalous commuting patterns: Mismatch between urban land attractiveness and commuting activities," Journal of Transport Geography, Elsevier, vol. 116(C).
    19. Ma, Zhenliang & Koutsopoulos, Haris N. & Liu, Tianyou & Basu, Abhishek Arunasis, 2020. "Behavioral response to promotion-based public transport demand management: Longitudinal analysis and implications for optimal promotion design," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 356-372.
    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).

    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:gam:jsusta:v:12:y:2020:i:4:p:1475-:d:321382. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.