Public transport disruptions can significantly affect passengers' travel behavior, yet little is known about how route choice preferences evolve after a disruption ends. This study investigates how passengers' route choices change in response to a planned disruption, using Automated Fare Collection (AFC) data from the Washington DC metro system. Unlike previous research that often relies on stated preference surveys or focuses only on the disruption period, this study analyzes several months of pre- and post-disruption behavior to assess whether changes in preferences persist over time.

Descriptive analysis reveals that although travel times and wait times remained relatively stable, passengers shifted from preferring direct routes to favoring routes with more transfers but shorter in-vehicle times after the disruption. Discrete choice models, including Multinomial Logit and Mixed Logit models, were estimated to explore these shifts, but the results showed unexpected coefficient signs, likely due to strong multicollinearity between travel time and transfers, and the presence of dominated alternatives. While the Mixed Logit model improved the model fit slightly, practical interpretability remained limited.

The findings suggest that disruptions can lead to lasting behavioral changes, with passengers reassessing their travel options rather than returning to previous habits. Although AFC data is valuable for detecting such shifts, it alone is insufficient to fully capture the factors driving route choice behavior, highlighting the need for complementary perception-based data in future research. From a policy perspective, understanding these behavioral adaptations can help transit agencies design better service recovery strategies that sustain ridership after disruptions, supporting broader goals of promoting sustainable urban mobility.