Different passenger demand rates in transit stations underscore the importance of adopting operational strategies to provide a demand-responsive service. Aiming at improving passengers’ travel time, the present study introduces a data-driven optimization approach to determine the optimal stop-skip pattern in urban rail transit lines. In detail, first, using the time-series smart card data, we employ a long short-term memory (LSTM) deep learning model to predict the station-level demand rates. Then, we integrate the output of the LSTM model as an input to an optimization model with the objective of minimizing patrons’ total travel time. Moreover, we utilize a heuristic algorithm to solve the problem in a desirable amount of time. Finally, the performance of the proposed models is assessed using real case data. The results suggest that the proposed approach can enhance the performance of the service by improving both passengers’ in-vehicle time as well as passengers’ waiting time.


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    Titel :

    A Deep-Learning Based Optimization Approach to Address Stop-Skipping Strategy in Urban Rail Transit Lines



    Kongress:

    International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington



    Erscheinungsdatum :

    2022-08-31




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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