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Uncovering spatiotemporal human mobility patterns in urban agglomerations: A mobility field based approach

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  • Yang, Hu
  • Lv, Sirui
  • Guo, Bao
  • Dai, Jianjun
  • Wang, Pu

Abstract

There are still lacking effective approaches to comprehensively quantify the attractions of different regions to human mobility. Here, we employ the mobility field approach to analyze the spatiotemporal human mobility patterns in the Changsha-Zhuzhou-Xiangtan urban agglomeration. The potential of each region of the mobility field is estimated to quantify the attraction of each region to human mobility. In addition, the fuzzy c-means algorithm is used to cluster the regions in the urban agglomeration. We find that residents from diverse regions of the urban agglomeration are attracted to work in Changsha (the core city of the urban agglomeration), and they usually return to their hometowns during holidays. We also find that the urban agglomeration splits into four regions, one of which covers the Changsha downtown and a part of Zhuzhou and Xiangtan, indicating a certain level of integration of the three cities. Our findings could assist policy makers to understand spatiotemporal human mobility patterns in urban agglomerations and guide urban agglomeration transportation management.

Suggested Citation

  • Yang, Hu & Lv, Sirui & Guo, Bao & Dai, Jianjun & Wang, Pu, 2024. "Uncovering spatiotemporal human mobility patterns in urban agglomerations: A mobility field based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    • Handle: RePEc:eee:phsmap:v:637:y:2024:i:c:s0378437124000797
    DOI: 10.1016/j.physa.2024.129571
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