How Has the Paris Rail Public-Transportation Network Recovered After the COVID-19 Pandemic? Applying a Mixture of Regressions Model

Abstract

Through a combination of regulations, fear of contagion, and changes in travelers’ habits, the COVID-19 pandemic affected the mobility of public-transit ridership worldwide. To understand the longer-term effects of the pandemic on public-transit ridership, we focus on the case of Paris, France, thanks to an open 5 year record of entries into more than 500 stations. To deal with the large volume of data, we use a statistical model that performs clustering and segmentation simultaneously while incorporating many exogenous variables, such as the day of the week or lockdowns, to account for their effect on the number of entries. We carry out an in-depth analysis of the results for the segments and clusters. Examining and comparing the regression coefficients across clusters and consecutive segments allows us to draw per-cluster and per-segment conclusions. We show that the number of weekday trips decreased in most clusters and that the reduction in weekly variations is proportional to the share of weekday trips in the volume of entries before the pandemic. In addition, we characterize the changes in the weekly profile: Thursday was replaced by Tuesday as the day with the highest ridership; because of teleworking, Friday became the least crowded weekday in clusters with strong differences between weekdays and weekends, while the lowest ridership weekday remains Monday in the other clusters.

Keywords

public transportation light rail transit rail transit systems subway urban big data smart-card data

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