Exploring the relationship between the determinants and the ridership decrease of urban rail transit station during the COVID-19 pandemic incorporating spatial heterogeneity

The study explores the relationship between the determinants and the ridership decrease incorporating spatial heterogeneity. ARIMA model is utilized to estimate the normal ridership assumed absence of COVID-19. Geography weighted regression (GWR) with Gaussian kernel function is constructed for regression. The K-means algorithm is applied to cluster the stations based on coefficients. Stations of Tokyo case are clustered into 2 groups: city area and western ward which represents mainly suburban areas. City stations are mainly influenced by the number of transfer lines, distance to the CBD, number of jobs and residents. In the western ward, the level of importance that residents place on public health primarily influences the ridership decrease. The implementation of work-from-home policies makes number of jobs a positive impactor on the decrease in ridership, with a greater impact observed on urban stations compared to suburban stations. City residents tend to engage in more travel than suburban residents because of less spacious living environments, which partially offsets the decrease in ridership. The findings offer parameters for predicting ridership of both city and suburban stations during public health emergency events, such as COVID-19. They can assist URT operators in developing strategies for balancing passenger demand and operational costs.