The aim of this research consisted of assessing the effects of the COVID-19 pandemic on the interurban public transport system in a rural region with a sparse population density, considering the number of tickets sold and passengers in each locality, as well as the different connecting lines. From a methodological point of view and with the intention of identifying patterns to explain the behaviour of both the routes and passengers, a series of variables were selected, becoming determining factors that sought to offer a solution to the search for a common trend. Additionally, data processing by the means of statistical analysis and the application of Geographic Information Systems (GIS) tools complemented the procedure. The results obtained in the investigation were provided both by municipality and by interurban routes. An interesting finding of this research was the uneven recovery of the municipalities. The localities closest to the attractor nucleus have recovered more quickly to pre-pandemic mobility levels due to their geographical proximity, larger populations, higher incomes per household, and need to access certain public services. In terms of routes, all the lines showed significant decreases in ticket sales, although with variations. Although passenger numbers have shown a gradual recovery, the initial loss was considerable, and pre-pandemic normality has not been completely achieved. This research provides a comprehensive overview of the changes in interurban mobility over a four-year period. The incorporation of critical variables and the segmentation by municipality and route provide a way to identify discernible patterns of mobility. However, the lack of previous research focusing on the impact of the pandemic in rural areas of low population density restricts the possibility of establishing a comparison and to generalise the findings. The authors consider that future research should include other alternative means of transport in these interurban areas and incorporate variables to characterise passengers, such as age, gender, etc.
