  COVID-19 has deeply affected daily life and travel behaviors. Understanding
these changes is crucial, prompting an investigation into socio-demographic and
socio-economic factors. This study used large-scale mobile device location data
in Washington, D.C., Maryland, and Virginia (DMV area) to unveil the impacts of
these variables on commute trip changes. It reflected short and long-term
impacts through linear regression and geographically weighted regression
models. Findings indicated that counties with a higher percentage of people
using walking and biking during the initial phase of COVID-19 experienced
greater reductions in commute trips. For the long-term effect in November, the
impact of active modes became insignificant, and individuals using public modes
showed more significant trip reductions. Positive correlations were observed
between median income levels and reduced commute trips. Sectors requiring
ongoing outdoor operations during the pandemic showed substantial negative
correlations. In the DMV area, counties with a higher proportion of Democratic
voters experienced less trip reduction. Applying Geographically Weighted
Regression models captured local spatial relationships, showing the emergence
of local correlations as the pandemic evolved, suggesting a geographical impact
pattern. Initially global, the pandemic's impact on commuting behaviors became
more influenced by spatial factors over time, showing localized effects.
