学术文献库

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic since some of the modeling assumption might be violated during this time. In this paper, utilizing change point detection procedures, we propose a piece-wise stationary time series model to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in New York City for randomly selected stations. Fitting the proposed model to these data sets enhances our understanding of ridership changes during external shocks, both in terms of mean (average) changes as well as the temporal correlations.