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Urban mobility analytics amid COVID-19 pandemic: A framework for promoting work resumption based on mobile phone data

Author

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  • He, Linghui
  • Li, Weifeng
  • Li, Jian
  • Sun, Jianping

Abstract

The Corona Virus Disease 2019 (COVID-19) pandemic had a pernicious influence on the whole world, so that the international community implemented various travel policies to inhibit the viral spread. With the COVID-19 pandemic under control, the overly lenient measures allowed the new variant to take advantage. Also, it is difficult to determine the geographical scope of transport policy that has the least impact on work resumption. Therefore, it is necessary to find policies to promote work resumption while preventing and controlling the pandemic in a geographical perspective. In this paper, a framework based on mobile phone data is proposed for promoting work resumption during the pandemic. First, human mobility networks are constructed through the origin-destination flow extracted from mobile phone data. Second, the singular value decomposition (SVD) algorithm is utilized to recognize the mobility patterns and explore the urban mobility dynamics of work-resumption-related travel during the pandemic. Third, the nonoverlapping community detection algorithm is used to generate policy zoning for work-resumption-related travel. Finally, the policy zoning is validated by public health data based on the overlay analysis and spatial statistics method, and policy implications are proposed. A case study of Shanghai, China is applied to verify the proposed framework. The results show that the framework can capture the mobility patterns and implement the policy zoning based on mobile phone data to balance the economic activity and public health during the pandemic, and the proposed framework can be applied to other cities with future public health events.

Suggested Citation

  • He, Linghui & Li, Weifeng & Li, Jian & Sun, Jianping, 2024. "Urban mobility analytics amid COVID-19 pandemic: A framework for promoting work resumption based on mobile phone data," Journal of Transport Geography, Elsevier, vol. 117(C).
    • Handle: RePEc:eee:jotrge:v:117:y:2024:i:c:s0966692324000966
    DOI: 10.1016/j.jtrangeo.2024.103887
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    References listed on IDEAS

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