Dynamic data-driven computation method for the number of waiting passengers and waiting time in the urban rail transit network

Abstract

Excess passengers gathering on the urban rail transit platform in a short time brings huge security risks to passengers and the daily operation of urban rail transit. However, the real-time monitoring method of passenger flows, which relies on manual methods, cannot satisfy the requirement of daily operations at the network level anymore. This study proposes a dynamic data-driven computation and monitoring method for the number of waiting passengers on platforms to recognize the operational risk in real-time. For waiting passengers on platforms, the waiting time duration before boarding is also calculated based on a first-come-first-service basis. It can be used to provide real-time information services to passengers and evaluate service quality. Moreover, the proposed methodology relies solely on the AFC data and the train timetable, which makes it easy to implement in the daily operation of any urban rail transit system. Finally, taking the Beijing rail transit network as a case study, the real-time number of waiting passengers on each platform, and the time duration passengers should wait before boarding are calculated dynamically. Meanwhile, the spatio-temporal distribution of passengers’ waiting time and waiting passengers are detailly analyzed based on the method of cluster analysis and the complex network theory.