Large-scale population movements can turn local diseases into widespread epidemics. Grasping the characteristic of the population flow in the context of the COVID-19 is of great significance for providing information to epidemiology and formulating scientific and reasonable prevention and control policies. Especially in the post-COVID-19 phase, it is essential to maintain the achievement of the fight against the epidemic. Previous research focuses on flight and railway passenger travel behavior and patterns, but China also has numerous suburban residents with a not-high economic level; investigating their travel behaviors is significant for national stability. However, estimating the impacts of the COVID-19 for suburban residents' travel behaviors remains challenging because of lacking apposite data. Here we submit bus ticketing data including approximately 26,000,000 records from April 2020-August 2020 for 2705 stations. Our results indicate that Suburban residents in Chinese Southern regions are more likely to travel by bus, and travel frequency is higher. Associated with the economic level, we find that residents in the economically developed region more likely to travel or carry out various social activities. Considering from the perspective of the traveling crowd, we find that men and young people are easier to travel by bus; however, they are exactly the main workforce. The indication of our findings is that suburban residents' travel behavior is affected profoundly by economy and consistent with the inherent behavior patterns before the COVID-19 outbreak. We use typical regions as verification and it is indeed the case.
Keywords: bus ticketing big data; chinese suburban residents; population flow; post-COVID-19 phases; spark distributed computing.