| Summary: | The outbreak of novel coronavirus disease 2019 (COVID-19) caused many consequences in almost all aspects of our lives. The pandemic dramatically changes people’s behavior in urban areas and transportation systems. Many studies have attempted to analyze spatial behavior and to present analysis data visually in the process of spreading COVID-19 and provided limited temporal and geographical perspectives. In this article, the behavioral changes in urban areas and transportation systems were analyzed throughout the U.S.A. while the COVID-19 spread over 2020. Specifically, assuming the characteristics are not repetitive over time, temporal phases were proposed where spikes or surges of confirmed cases are noticed. The interdependencies between population, mobility, and additional behavioral data were explored at the county level by adopting the machine learning approaches. As a result, interdependencies with the COVID-19 cases were identified differently by phase. It appeared to have a solid relationship with population size at all phases. Furthermore, it revealed racial characteristics, residential types, and vehicle mile traveled ratio in the urban and rural areas had a relationship with confirmed cases with different importance by phase. Although other short-term analyses were also conducted in terms of the COVID-19, this article is considered more legitimate as it provides dynamic relationships of urban elements by Phase at the county level. Moreover, it is expected to be encouraging and beneficial in terms of phase-driven transportation policy preparedness against a possible forthcoming pandemic crisis.
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