Passenger Travel Behavior Analysis under Unplanned Metro Service Disruption: Using Stated Preference Data in Guangzhou, China

Abstract

Passengers may change their original travel plans when unplanned metro service disruption occurs, which causes excessive crowding and endangers the safety of passengers. However, it’s not clear about the mechanism of passengers’ behaviors under unplanned service disruption and what factors affect mode shift and travel plan choice behavior. Based on the stated preference data in Guangzhou, China, this paper analyzes the travel plan choice behavior under unplanned service disruption by using a nested logit model. The nested structure consists of two levels: the upper level represents the mode shift choice, and the lower level shows travel plan choice corresponding to the mode shift or not. The results indicate that service disruption attributes and individual attributes are significant predictors to the mode shift. Furthermore, passengers are more sensitive to intermode transfers compared with interline transfers when disruption occurs. The proposed model is also applied to forecast passenger flow volume of metro stations under disruption, and the prediction result indicates that the proposed model well captures passenger mode shift and travel plan choice behavior under unplanned service disruption.