Understanding urban traffic flows in response to COVID-19 pandemic with emerging urban big data in Glasgow

Abstract

Urban traffic analysis has played an important role in urban development, providing insights for urban planning, traffic management, and resource allocation. Meanwhile, the global pandemic of COVID-19 has significantly changed people's travel behaviour in urban areas. 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 include factors such as road characteristics, socio-demographics, surrounding built environments (land use and nearby points of interest), and the emerging urban big data source of Google Street View images to understand their influences on time series traffic flows. Taking the city of Glasgow as the case study, we have found that areas with more young and white dwellers are associated with more traffic flows, while natural green spaces are associated with fewer traffic flows. Major roads between cities and towns also show heavier traffic flows. Besides, the application of Google Street View images in this research has revealed the heterogeneous effects of green space on urban traffic flows, as the magnitudes of their effects vary by distance. We also detect that the spatial dependence between adjacent neighbourhoods among the traffic flows and associated urban parameters is variable during the four COVID-19 periods. With the influence of COVID-19, there has been a significant decrease in long-distance travel. The noticeable change in travel behaviour presents a valuable opportunity to encourage active travel in the near future.