HIGHLIGHTS Deal with the unfairness problem of passenger waiting times in an oversaturated URT line from the point of view of the individual time-dependent passenger demand through timetabling optimization Develop an approximate general model with a min-max type objective function to formulate the travel schemes of passengers and train timetabling (including the stop patterns) by clustering passengers into groups, where the problem scale is controlled by the defined aggregation granularity parameter Design a preprocessing method to reduce the space of feasible solutions for small-scale cases and a solving algorithm based on variable neighborhood search for large-scale cases To justify the choice of our min-max type objective function, a computational comparison between our model and the efficiency-oriented model was made. The performance of our VNS and the impact of the aggregation granularity parameter were investigated for different scales in both small instances and a real case study

    Abstract In oversaturated urban rail transit systems, passengers departing from downstream stations often experience long waiting times due to unbalanced space-time demand and limited transit capacity. This is often prevalent during morning and evening peak periods in transit systems. This paper aims to mitigate the unfairness of waiting time among a time-varying number of passengers through train timetable's adjustment by optimizing the train skip-stopping pattern. We develop an approximate general model by clustering passengers into groups and introducing an aggregation granularity parameter. To characterize feasible passenger travel patterns, both rigid first-in-first-out rule and capacity constraints are incorporated in the proposed model. Preprocessing is proposed to reduce the space of solutions. Some small-scale case studies show that the proposed method outperforms the original timetable and the preprocessing is effective to reduce computation time. Case studies based on the Batong line of Beijing rail transit network are conducted, in which a variable neighborhood search algorithm is applied to obtain high-quality solutions in short computing times. The results show that the proposed approach not only mitigates the unfairness of waiting time among passengers but also improves other efficiency evaluation indexes, including the average waiting time and the maximum number of missed trains. We also investigate the impact of the aggregation granularity parameter on the computational effort and solution accuracy.


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    Title :

    Mitigating unfairness in urban rail transit operation: A mixed-integer linear programming approach


    Contributors:
    Wu, Yinghui (author) / Yang, Hai (author) / Zhao, Shuo (author) / Shang, Pan (author)


    Publication date :

    2021-04-28


    Size :

    25 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English