# Who continued travelling by public transport during COVID-19? Socioeconomic factors explaining travel behaviour in Stockholm 2020 based on smart card data

## TL;DR

This study explores how socioeconomic factors influenced public transport use during the pandemic in Stockholm using smart card data.

## Contribution

The paper introduces two binomial logit models to analyze how socioeconomic factors affect travel behavior during the pandemic.

## Key findings

- All socioeconomic factors studied were linked to changes in public transport use during the pandemic.
- Differences in travel behavior between socioeconomic groups diminished by autumn 2020.
- Lower-income and immigrant-heavy areas reduced public transport use similarly to more affluent areas.

## 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 travelling 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 binomial logit models for explaining the change in travel pattern, linking socioeconomic data per area and travel data with the probability to stop travelling.

The first model investigates 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 all these factors.

The second model groups the investigated areas into five distinct clusters based on the socioeconomic data, showing the impacts for different socioeconomic groups. During the autumn the differences between the groups diminished, and especially Cluster 1 (with the lowest education levels, lowest income and highest share of immigrants) reduced their public transport use to a similar level as the more affluent clusters.

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.

The online version contains supplementary material available at 10.1186/s12544-021-00488-0.