The study analyzes travel patterns in Houston, Texas during COVID-19 using an autoregressive distributed lag model. Findings reveal that visit patterns and changes in COVID-19 cases from the previous week heavily influence behaviors in the following week. Factors such as unemployment claims, median minimum dwell time, and workplace visit activity significantly predict total foot traffic, while transit systems show an overall decrease in usage but are not significant in estimating foot traffic. This model offers a unique approach to quantifying and analyzing travel behaviors in response to COVID-19 in Houston.
