Analyzing demand reduction and recovery of major rail stations in Japan during COVID-19 using mobile spatial statistics

Abstract

Mobile spatial statistics from across Japan are used to analyze the vitality of stations over the COVID period. Time series population information of 500m × 500m meshes that include major stations are extracted. We analyze the demand loss patterns of 69 train stations during the first COVID wave. We firstly discuss the correlation of this data with annual ridership information. We then conduct a clustering analysis of the time series data and distinguish five impact patterns which we try to explain with a multinomial logistic regression. Stations in large cities had higher ridership but were also more affected than smaller cities. We also find that cities with less dense populations and more local population frequenting the station appear to be more robust to the pandemic. Our results can be used to help cities forecasting the impact of future pandemics on the local economy.