This paper mainly focuses on the method of timetable multi-objective optimization considering the randomness of passengers flow. The passenger flow model is established based on the normal distribution of the passenger flow, which is verified through the testing hypotheses for the number of passengers getting on and off the train. In assessment of energy consumption, the train operation between the stations is assumed as the optimal strategy, which is described as four stages according to Pontryagin maximum principle: maximum acceleration, speed holding, coasting and maximum braking. Then an improved GA (genetic algorithm) is designed to obtain the optimal solution. Finally, this paper test and verify the effectiveness of the optimal model through the simulation, taking the running time, departure interval, dwell time as decision variables, based on the experimental data from the Beijing subway with the conventional GA and the improved GA respectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Timetable multi-objective optimization by improved genetic algorithm based on stochastic passenger flow


    Beteiligte:
    Sun, Xubin (Autor:in) / Hong Lu, (Autor:in) / Dong, Hairong (Autor:in) / Jing Xun, (Autor:in)


    Erscheinungsdatum :

    2016-11-01


    Format / Umfang :

    269460 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch