Reliability Measure-Based Data Analytics Approach to Identifying and Ranking Recurrent Bottlenecks in Urban Rail Transit Networks

Abstract

Recurrent bottlenecks in the urban rail transit (URT) system during rush hours have become a significant concern for both passengers and URT operators. In order to alleviate the resulting peak-hour congestion in urban rail transit networks in a more effective and efficient manner, a systematic reliability measure-based data analytics method is developed to identify and rank recurrent bottlenecks at the network level. The reliability measure applied in this study is the frequency of congestion (FoC) measure, which is specially redefined for use in the URT system. The Shanghai Metro system is used to conduct a real-world case study to validate the proposed approach. Based on the passenger trip data collected at 5-min time intervals by the automatic fare collection (AFC) system and train diagrams on workdays in the Shanghai Metro system, the values of newly defined FoC are computed using two different thresholds for both morning and evening peak periods. Research results of the Shanghai Metro system indicate that it is a feasible and effective way to apply FoC values in identifying and ranking bottlenecks in the URT network. Impacts of using different threshold values in defining FoC is studied, and comparison results between FoC and the peak-hour load factor (PLF), which is the other commonly-used identification indicator, are also provided. The methodology developed and detailed information on bottlenecks presented in this study can greatly help decision makers and operators in the URT system to evaluate the crowding conditions along rail segments and develop targeted bottleneck mitigation solutions in a more effective and efficient manner.