This paper has established an urban computing framework T2NBS (Taxi to Night-time Bus Service) in order to realize the optimal NBS (Night-time Bus Service) system. The framework includes (1) a reasonable mathematical model MNL for travel demand estimation and a multi-variable decision target MINLP for the optimal system output, which simultaneously optimize the NBS system’s design quality (site, path, schedule, vehicle-to-passenger matching) and service quality (walking accessibility, demand-matching degree, travel time and travel cost, the profit of the whole system); and (2) a staged sequential heuristic algorithm for solving the programming model’s “super integrated NP-Hard” problem, including the sub-algorithm for mining the night-time collective travel demands from taxi trajectories at the first stage, the second-stage deployment sub-algorithm for extracting the fewest sites with best walking accessibility, and the third-stage sub-algorithm for searching the best operation timetable with maximized revenues.


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    Titel :

    T2NBS: Planning Night-Time Demand-Oriented Bus Systems with Urban Computing Approaches


    Beteiligte:
    Wang, JianJun (Autor:in) / Qu, Lei (Autor:in) / Wang, Di (Autor:in) / Liu, YunFei (Autor:in)

    Kongress:

    20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)


    Erschienen in:

    CICTP 2020 ; 3457-3469


    Erscheinungsdatum :

    2020-08-12




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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