Modeling disruption durations of subway service via random survival forests: The case of Shanghai

Subways are the backbone of many urban transportation systems in large cities around the world. However, disruptions of subway services, such as power-supply failure or signal failures, can cause severe travel delays due to the large carrying capacities of subway trains. Accurate predictions of the disruption durations of subway services are essential for emergency response. To predict the disruption durations of subway services, previous studies primarily used parametric models (e.g., the accelerated failure time (AFT) model), and less attention has been given to machine-learning models with high potential prediction ability and fewer parameter restrictions. This paper proposes a model to predict and explore factors that affect the disruption durations of subway services in Shanghai using machine-learning models. The longitudinal data released by the subway operator from 2012 to 2021 were collected and analyzed in this study. A random survival forest (RSF) was used to describe the disruption durations of the subway service, and influential factors, such as incident reason, incident occurrence time, and line-related variables were considered in the model. Results show that the RSF model (C-Index = 0.672) achieved better prediction accuracy than the traditional AFT model (the best C-Index = 0.609) based on the collected data in Shanghai. In addition, results indicate that incident reason, disruption location, and the time of disruption factors can significantly affect subway service disruption durations. The proposed model can be used as a tool to predict the disruption durations of subway service for better disruption management of the subway system.