Analysis on the effect of COVID-19 measures on human mobility in downtown of local city using transportation big data: Focusing on the difference between the multiple state of emergency

During the COVID-19 pandemic, decreasing human mobility remains an essential measure despite the start of vaccinations due to the disease’s long incubation period and high infectivity. This study aims to investigate the effects of the 1st state of emergency, prefectural state of emergency, and 2nd state of emergency on human mobility during the COVID-19 pandemic based on the mobile spatial statistics and the pedestrian count data by applying the Bayesian structural time series model. First, we quantified the effects of different states of emergency regarding both under and after the declaration. Second, comparing the two data sets shows that the staying time reduced after the 1st state of emergency even though the number of pedestrians recovered the previous level. Third, the changes in human mobility consistent with the existing surveys on remote work and behaviors during the COVID-19 pandemic.