Exploring the Footprint of COVID-19 on the Evolution of Public Bus Transport Demand Using GIS

The scope of the research work described in this article involved identifying the effects of the COVID-19 pandemic on the urban public transport system in a medium-sized city and its adjacent metropolitan area, using as reference information the number of tickets effectively sold in order to determine the fluctuation in the volume of passengers on the different bus lines before, during and after the pandemic. At the methodological level, a combined approach was employed, involving, on the one hand, the collection of open access public data from institutional repositories and information provided by the government and, on the other hand, network analysis and graphical mapping using GIS tools. The results obtained at the micro level (individualised study of each urban bus line) reveal a significant decrease in the number of passengers during the pandemic, showing the effect of mobility restrictions and the fear of contagion. However, a gradual recovery in post-pandemic demand has been observed, highlighting a large variability in recovery patterns between different bus lines. Such a situation could be attributable to several factors, such as the socio-demographic characteristics of the areas served, the frequency of the service, connectivity with other modes of transport and users’ perception of the quality of the service. At the macro level (comparison between urban and interurban transport), lines with higher demand prior to the pandemic have shown greater resilience and faster recovery. However, urban transport has experienced a more uniform and accelerated recuperation than interurban transport, with significant percentage differences in the years analysed. This disparity could be explained by the greater dependence of inhabitants on urban transport for their daily trips, due to its greater frequency and geographical coverage. Interurban transport, on the other hand, shows a more fluctuating demand and a lower dependence of users. Finally, the lack of previous research focused on the impact of the pandemic in sparsely populated rural areas restricts the ability to establish a solid frame of reference and generalise the results of this study. The authors consider that more detailed future research, including a comparative analysis of different alternative transport modes in inter-urban settings and considering a broader set of socio-demographic variables of passengers, is needed to better understand mobility dynamics in these areas and their evolution in the context of the pandemic.