Who Continued Traveling by Public Transport During COVID-19? Socioeconomic Factors Explaining Travel Behaviour in Stockholm 2020 Based on Smart Card Data

Almlöf, Erik and Rubensson, Isak and Cebecauer, Matej and Jenelius, Erik, Who Continued Traveling by Public Transport During COVID-19? Socioeconomic Factors Explaining Travel Behaviour in Stockholm 2020 Based on Smart Card Data (January 20, 2021). Available at SSRN: https://ssrn.com/abstract=3689091 or http://dx.doi.org/10.2139/ssrn.3689091

Abstract

The COVID-19 pandemic has changed travel behaviour and reduced the use of public transport throughout the world, but the reduction has not been uniform. In this study we analyse the propensity to stop traveling by public transport during COVID-19 for the holders of 1.8 million smart cards in Stockholm, Sweden, for the spring and autumn of 2020. We suggest two models for explaining the change in travel pattern, linking socioeconomic data per area and travel data with the probability to stop traveling.

The first model analyses the impact of the socioeconomic factors: age; income; education level; gender; housing type; population density; country of origin; and employment level. The results show that decreases in public transport use are linked to areas with a population of high socioeconomic status (e.g. income levels, owned houses and high employment levels).

The second model groups the investigated areas into five distinct clusters based on the socioeconomic data: Rural, Working Class, Impoverished, Garden Suburbs and Central. During spring, residents in the Rural group were most likely to continue traveling, followed by the Impoverished, Working Class, Garden Suburbs and Central. During fall the differences between the groups diminished, and especially the Impoverished group reduced their public transport use to a similar level as the Garden Suburbs group.

The results show that socioeconomic status affect the change in behaviour during the pandemic and that exposure to the virus is determined by citizens’ socioeconomic class. Furthermore, the results can guide policy into tailoring public transport supply to where the need is, instead of assuming that e.g. crowding is equally distributed within the public transport system in the event of a pandemic.

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