The impacts of COVID-19 on route choice with guidance information in urban rail transit of megacities

The outbreak of COVID-19 has caused unprecedented decline of ridership in urban rail transit and changed passenger travel habits, which greatly challenges subway operations. Therefore, it is necessary to better understand and quantify the impact of COVID-19 on passenger travel behavior, specifically route choice. Thus, we collected automatic fare collection data and 2060 random samples through a web-based survey in Beijing on passengers' route choice behavior during the COVID-19 pandemic. This study utilizes an initial dataset to conduct an analysis and introduces an improved Generalized Random Regret Minimization model (GRRM) aimed at understanding passengers' route choice adjustments in response to COVID-19 guidance information. This improved GRRM accounts for two decision-making criteria, namely, maximum utility and minimum regret, and considers passenger heterogeneity. This marks the first instance of capturing the heterogeneity shift effect in route choice perception during the COVID-19 pandemic. The results show that the improved model has the best fitting result with adjusted Rho square of 0.536, demonstrating that the attributes related to guidance information (i.e., information push/time to receive traffic information/perceived route COVID-19 risk) indeed enhance the model's fit. Furthermore, the research employs Value of Information Time to quantify the preference of passengers for information in various groups. Compared with the usual scenario, women, young and non-commuter passengers are more likely to receive an early information update to plan their trips in advance. Finally, the perceived risk of COVID-19 on routes is examined in relation to passengers' personal attributes. It is observed that the elderly and students exhibit heightened sensitivity to the epidemic at all stages, while young passengers and commuters are particularly sensitive only during the small-scale epidemic. These findings offer valuable insights for managers to implement targeted strategies, thereby enhancing passenger flow control and encouraging increased subway ridership.