Modeling Urban Railway Passengers’ Willingness to Wait Based on Latent Class Analysis

Abstract

Overcrowding in confined spaces, like inside trains, is a social issue that, in the long-term, could lead to accidents. To alleviate congestion in trains, a reduction in train headways is planned, made possible by technological advances such as the introduction of advanced autonomous trains (e.g., 60-s headway). However, simply reducing a train’s headway may be challenging because this does not consider the travel behavior of passengers who engage in the disruptive act of excessively boarding trains, thereby causing delays in their departure. Furthermore, the behavior of passengers may vary depending on an individual’s latent preferences, however, there is insufficient research that reflects these latent preferences. In this study, a survey was conducted with 971 urban rail passengers in the Seoul Metropolitan Area of Korea to identify their latent preferences using latent class analysis, resulting in a four-type classification: a congestion avoidance type, a time-sensitive type, a subway-preference type, and a type that does not prefer public transportation. Based on this, willingness to wait (WTW; i.e., waiting for the next less crowded train to reduce passenger discomfort) according to their latent preferences, was calculated. WTW was found to increase as the headway of the train was reduced. Choice modeling was conducted based on variables, including socioeconomic variables, to suggest effects related to WTW. The results indicated demand dispersion effects according to the headway reduction at peak times for various types of people. This study’s findings could be utilized to offer less crowded and safer train operations.