AIDC project number:
Ahmed Abdel-Rahim
Kevin Chang
US DOT UTC: CSET
The COVID-19 pandemic caused unprecedented disruptions to human mobility and transportation systems worldwide, significantly altering travel behavior and mode choices. This study investigates these changes within the Pacific Northwest region of the United States, encompassing a mix of urban and rural contexts with diverse socio- demographic characteristics. Using survey data from 807 respondents, we analyze transportation patterns before and during the pandemic, focusing on shifts in mode shares and probabilities of switching travel modes. The analysis incorporates McNemar’s test, logistic regression, and latent class analysis (LCA) to evaluate the extent of these shifts and identify key influencing factors. The results reveal a substantial reduction in public transport usage, reflecting heightened concerns over health risks and limited operational capacity during the pandemic. In contrast, there was a notable increase in the use of private vehicles and active transportation modes, such as walking and cycling. Demographic variables, including age, income, employment status, and gender, played significant roles in shaping travel behavior, with younger and lower-income individuals exhibiting higher probabilities of mode change. The latent class analysis highlighted distinct behavioral clusters, indicating that travel behavior responses were not uniform across populations. A logistic regression model further underscored the importance of pre-pandemic travel habits, socio-economic conditions, and pandemic-related concerns in influencing mode choice decisions. Additionally, traffic safety outcomes showed notable variations, with overall crash rates decreasing during the lockdowns but fatality rates rising due to riskier driving behaviors, such as speeding on roads. Crash patterns varied across urban and rural areas, with urban crashes experiencing a slight decline in proportion, while rural crashes increased.
