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The COVID-19 pandemic in 2020 has caused sudden shocks in transportationsystems, specifically the subway ridership patterns in New York City.Understanding the temporal pattern of subway ridership through statisticalmodels is crucial during such shocks. However, many existing statisticalframeworks may not be a good fit to analyze the ridership data sets during thepandemic since some of the modeling assumption might be violated during thistime. In this paper, utilizing change point detection procedures, we propose apiece-wise stationary time series model to capture the nonstationary structureof subway ridership. Specifically, the model consists of several independentstation based autoregressive integrated moving average (ARIMA) modelsconcatenated together at certain time points. Further, data-driven algorithmsare utilized to detect the changes of ridership patterns as well as to estimatethe model parameters before and during the COVID-19 pandemic. The data sets offocus are daily ridership of subway stations in New York City for randomlyselected stations. Fitting the proposed model to these data sets enhances ourunderstanding of ridership changes during external shocks, both in terms ofmean (average) changes as well as the temporal correlations.

Non-Stationary Time Series Model for Station Based Subway Ridership During Covid-19 Pandemic (Case Study: New York City)