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.

中文翻译：

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谁在 COVID-19 期间继续乘坐公共交通工具旅行？基于智能卡数据解释斯德哥尔摩 2020 旅行行为的社会经济因素

COVID-19 大流行改变了旅行行为并减少了世界各地公共交通的使用，但这种减少并不统一。在这项研究中，我们分析了 2020 年春季和秋季瑞典斯德哥尔摩 180 万张智能卡持有者在 COVID-19 期间停止乘坐公共交通工具的倾向。我们建议使用两个二项式 logit 模型来解释旅行的变化模式，将每个地区的社会经济数据和旅行数据与停止旅行的可能性联系起来。第一个模型调查社会经济因素的影响：年龄；收入; 教育程度; 性别; 住房类型；人口密度; 原产地; 和就业水平。结果表明，公共交通使用的减少与所有这些因素有关。第二个模型根据社会经济数据将调查区域分为五个不同的集群，显示对不同社会经济群体的影响。秋季期间，各组之间的差异缩小，尤其是第 1 组（教育水平最低、收入最低和移民比例最高）将其公共交通使用量降至与更富裕的组相似的水平。结果表明，社会经济地位会影响大流行期间的行为变化，而病毒的暴露程度取决于公民的社会经济阶层。此外，结果可以指导政策根据需要调整公共交通供应，而不是假设在大流行的情况下，公共交通系统内的拥挤情况是平均分布的。