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.
KopsidasA.MiliotiC.KepaptsoglouK.VlachogianniE. I.How Did the COVID-19 Pandemic Impact Traveler Behavior Toward Public Transport? The Case of Athens, Greece. Transportation Letters, Vol. 13, No. 5–6, 2021, pp. 344–352. https://doi.org/10.1080/19427867.2021.1901029.
MützelC. M.ScheinerJ.Investigating Spatio-Temporal Mobility Patterns and Changes in Metro Usage Under the Impact of COVID-19 Using Taipei Metro Smart Card Data. Public Transport, Vol. 14, No. 2, 2022, pp. 343–366. https://doi.org/10.1007/s12469-021-00280-2.
12.
AlmlöfE.RubenssonI.CebecauerM.JeneliusE.Who Continued Travelling by Public Transport During COVID-19? Socioeconomic Factors Explaining Travel Behaviour in Stockholm 2020 Based on Smart Card Data. European Transport Research Review, Vol. 13, No. 1, 2021, p. 31. https://doi.org/10.1186/s12544-021-00488-0.
JiaoJ.HansenK.AzimianA.Disparities in the Impacts of the COVID-19 Pandemic on Public Transit Ridership in Austin, Texas, U.S.A. Transportation Research Record: Journal of the Transportation Research Board, 2023. 2677: 287–297.
ManoutO.BouzouinaL.KourtitK.NijkampP.On the Bumpy Road to Recovery: Resilience of Public Transport Ridership During COVID-19 in 15 European Cities. Letters in Spatial and Resource Sciences, Vol. 16, No. 1, 2023, p. 14. https://doi.org/10.1007/s12076-023-00338-8.
de NaillyP.CômeE.SaméA.OukhellouL.FerriereJ.Merad-BoudiaY.What Can We Learn from 9 Years of Ticketing Data at a Major Transport Hub? A Structural Time Series Decomposition. Transportmetrica A: Transport Science, Vol. 18, No. 3, 2021, pp. 1–25. https://doi.org/10.1080/23249935.2021.1948626.
RasmussenC. E.Gaussian Processes in Machine Learning. In Proc., Advanced Lectures on Machine Learning: ML Summer Schools 2003: Lecture Notes in Computer Science (BousquetO.von LuxburgU.RätschG., eds.), Canberra, Australia, February 2–14, 2003, Tübingen, Germany, August 4–16, 2003, Revised Lectures, Springer, Berlin, Heidelberg, 2004, pp. 63–71. https://doi.org/10.1007/978-3-540-28650-9_4.
31.
BaudryJ.-P.MaugisC.MichelB.Slope Heuristics: Overview and Implementation. Statistics and Computing, Vol. 22, No. 2, 2012, pp. 455–470. https://doi.org/10.1007/s11222-011-9236-1.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
