Study of travel behavior during the covid-19 pandemic

This study examines the impact of the COVID-19 pandemic on residents' commuting patterns in the United States. Using the MNL (Multinomial Logit) and binary logit models, we analyze the factors influencing the choice of commuting modes before and during the pandemic. Our findings indicate that various personal, travel-related, and COVID-19-related factors significantly affect commuting choices. For commuting methods other than driving, factors such as gender, age, possession of a driver's license, bicycle ownership, car ownership, family size, working days per week, COVID-19 testing, and mask restrictions play a significant role. The decision to walk to work is influenced by gender, vehicle ownership, travel time, travel distance, working days per week, family income, COVID-19-related relocation, and level of COVID-19 anxiety. Public transportation choices are influenced by factors such as age, race, possession of a driver's license, car ownership, travel time, travel distance, COVID-19-related migration, and COVID-19 testing of cohabitants. Furthermore, the binary logit model reveals that personal factors (e.g., gender, driver's license) and COVID-19-related factors (e.g., mask restrictions, level of concern about the coronavirus) significantly impact the consistency of travel modes before and during the pandemic. This study contributes to our understanding of the changes in commuting patterns during the COVID-19 pandemic and provides insights into the factors that shape residents' commuting choices. The findings can inform transportation planning and policy-making to promote sustainable and resilient transportation systems in the face of future disruptions