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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
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    References listed on IDEAS

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    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.
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    Cited by:

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    5. 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.

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