We present a probabilistic proactive rebalancing method and speed-up techniques for improving the performance of a state-of-the-art real-time high-capacity fleet management framework. We improve on both computational efficiency and system performance. The speed-up techniques include search-space pruning and I/O cost reduction for parallelization, reducing the computation time by up to 97.67%, in experiments on taxi trips in New York City. The proactive rebalancing routes idle vehicles to future demands based on probabilistic estimates from historical demand, increasing the service rate by 4.8% on average, and decreasing the waiting time and total delay by 5.0% and 10.7% on average, respectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Proactive Rebalancing and Speed-Up Techniques for On-Demand High Capacity Ridesourcing Services


    Beteiligte:
    Liu, Yang (Autor:in) / Samaranayake, Samitha (Autor:in)


    Erscheinungsdatum :

    01.02.2022


    Format / Umfang :

    634404 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Proactive empty vehicle rebalancing for Demand Responsive Transport services

    Bischoff, Joschka / Maciejewski, Michał | DataCite | 2020

    Freier Zugriff

    Investigating Older Adults’ Propensity toward Ridesourcing Services

    Sarker, Md. Al Adib / Rahimi, Alireza / Azimi, Ghazaleh et al. | ASCE | 2022


    A Model of Ridesourcing Demand Generation and Distribution

    Lavieri, Patrícia S. / Dias, Felipe F. / Juri, Natalia Ruiz et al. | Transportation Research Record | 2018



    Exploring the attitudes of Millennials and Generation Xers toward ridesourcing services

    Azimi, Ghazaleh / Rahimi, Alireza / Jin, Xia | Online Contents | 2021