The Covid-19 pandemic drastically changed urban mobility, both during the height of the pandemic with government lockdowns, but also in the longer term with the adoption of working-from-home policies. To understand its effects on rail public transport ridership, we propose a dedicated Regression Mixture Model able to perform both the clustering of public transport stations and the segmentation of time periods, while ignoring variations due to additional variables such as the official lockdowns or non-working days. Each cluster is thus defined by a series of segments in which the effect of the exogenous variables is constant. As each segment within a cluster has its own regression coefficients to model the impact of the covariates, we analyze how these coefficients evolve to understand the changes in the cluster. We present the regression mixture model and the parameter estimation using the EM algorithm, before demonstrating the benefits of the model on both simulated and real data. Thanks to a five-year dataset of the ridership in the Paris public transport system, we analyze the impact of the pandemic, not only in terms of the number of travelers but also on the weekly commute. We further analyze the specific changes that the pandemic caused inside each cluster.

新冠疫情对城市交通和公共交通乘客数量造成了重大影响，本研究提出一种专用的回归混合模型，能够对公共交通站点进行聚类和时间段进行分割，以分析疫情对铁路公共交通乘客数量的影响，并分析疫情引起的变化。

一个回归混合模型了解新冠疫情对公共交通乘客量的影响