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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2020-07-11




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown



    Data-driven approach for identifying spatiotemporally recurrent bottlenecks

    Song, Tai-Jin / Williams, Billy M. / Rouphail, Nagui M. | IET | 2018

    Free access

    Data‐driven approach for identifying spatiotemporally recurrent bottlenecks

    Song, Tai‐Jin / Williams, Billy M. / Rouphail, Nagui M. | Wiley | 2018

    Free access


    SYSTEMS AND METHODS FOR IDENTIFYING AND RANKING TRAFFIC BOTTLENECKS

    MA YUNFEI / LIU CHIEN AN | European Patent Office | 2024

    Free access

    SYSTEMS AND METHODS FOR IDENTIFYING AND RANKING TRAFFIC BOTTLENECKS

    MA YUNFEI / LIU CHIEN AN | European Patent Office | 2024

    Free access