This paper focuses on the timetabling problem for a congested urban rail transit network. During peak hours, the situation of premature full load of trains and passenger stranded on platforms is serious. To minimize the total passenger’s travelling time and optimize resource allocation from a planning and management perspective, we develop an optimization model considering dwelling time as one of the decision variables to control the number of boarding passengers. In the proposed model, the network internal passenger flow is quantitatively characterized by three types of flows according to its origin and destination under the distributed structure. And the mixed logic dynamic model describes the operation of network, especially the interaction between lines. Furthermore, the number of time-varying transfer passengers is calculated which has never been studied before. At last, we adopt a simulation-based approach to solve the model and prove the effectiveness through two cases.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Timetabling for a congested urban rail transit network based on mixed logic dynamic model


    Beteiligte:
    Hao, Sijia (Autor:in) / Song, Rui (Autor:in) / He, Shiwei (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    31.12.2022


    Format / Umfang :

    20 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Energy-efficient timetabling for urban rail transit network considering passenger path choice behaviors

    Huang, Kang / Liao, Feixiong / Gao, Ziyou | TIBKAT | 2022

    Freier Zugriff

    Rolling Stock Allocation and Timetabling for Urban Rail Transit Network with Multiple Depots

    Pu, Fan / Yin, Jiateng / Wang, Yihui et al. | Transportation Research Record | 2022



    Congested Situation Identification of Urban Rail Transit Carriage Based on Deep Learning

    Wang, Bo / Yang, Guixin / Zhou, Jinyao et al. | ASCE | 2020


    Congested Situation Identification of Urban Rail Transit Carriage Based on Deep Learning

    Wang, Bo / Yang, Guixin / Zhou, Jinyao et al. | TIBKAT | 2020