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Uncovering stable and occasional human mobility patterns: A case study of the Beijing subway

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  • Yong, Nuo
  • Ni, Shunjiang
  • Shen, Shifei
  • Chen, Peng
  • Ji, Xuewei

Abstract

There have generally been two kinds of approaches to the empirical study of human mobility. At the group level, some valuable information might be submerged in statistical noise, while due to the diversity of individual purpose and preference, there is still no general statistical regularity of human mobility at the individual level. In this paper, we considered group-level human mobility as the combination of several basic patterns and analyzed the collective mobility by category. Utilizing matrix factorization and correlation analysis, we extracted some of the stable/occasional components from the collective human mobility in the Beijing subway and found that the departure and arrival mobility patterns have different characteristics, both in time and space, under various conditions. We classified individual records into different patterns and analyzed the most likely trip distance by category. The proposed method can decompose stable/occasional mobility patterns from the collective mobility and identify passengers belonging to different patterns, helping us to better understand the origin of different mobility patterns and provide guidance for emergency management of large crowds.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:28-38
    DOI: 10.1016/j.physa.2017.09.082
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    Citations

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    Cited by:

    1. 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).
    2. 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).
    3. Yong, Nuo & Ni, Shunjiang & Shen, Shifei & Ji, Xuewei, 2020. "A study of fluctuations in subway traffic from the control properties of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    4. 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).
    5. 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.
    6. Maosheng Li & Hangcong Li, 2022. "Optimal Design of Subway Train Cross-Line Operation Scheme Based on Passenger Smart Card Data," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    7. 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.
    8. 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.

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