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Understanding Urban Traffic Flows in Response to COVID-19 Pandemic with Emerging Urban Big Data in Glasgow

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  • Li, Yue
  • Wang, Mingshu
  • Zhao, Qunshan

Abstract

With the global pandemic significantly changing people’s travel behaviours, urban traffic analysis has played an even more important role in urban (re)development, providing insights for urban planning, traffic management, and resource allocation. This research uses the spatial Durbin model to understand the relationship between traffic flows, urban infrastructure, and socio-demographic indicators before, during, and after pandemic periods. We analyze factors including road characteristics, socio-demographics, surrounding built environments, and Google Street View images to understand their influences on traffic flows. In Glasgow, we have found that areas with more young and white dwellers are associated with higher traffic flows, while green spaces are associated with fewer traffic flows. The application of Google Street View images has revealed the heterogeneous effects of the built environment on urban traffic flows, as the magnitudes of their effects vary by distance. With the influence of COVID-19, residents prefer to spend their daily life in their local areas rather than having long-distance travel in the pre-pandemic time. With this noticeable travel behaviour change, the promotion and development of the 15 or 20-minute neighbourhood concept can play an important role in encouraging active travel and achieving a net-zero carbon target in the near future.

Suggested Citation

  • Li, Yue & Wang, Mingshu & Zhao, Qunshan, 2023. "Understanding Urban Traffic Flows in Response to COVID-19 Pandemic with Emerging Urban Big Data in Glasgow," OSF Preprints kwbdz, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:kwbdz
    DOI: 10.31219/osf.io/kwbdz
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    References listed on IDEAS

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    1. J. Paul Elhorst, 2014. "Spatial Panel Data Models," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 37-93, Springer.
    2. Bai, Jushan & Li, Kunpeng, 2013. "Spatial panel data models with common shocks," MPRA Paper 52786, University Library of Munich, Germany, revised 09 Mar 2014.
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