Autonomous trucks are expected to fundamentally transform the freight transportation industry. In particular, Autonomous Transfer Hub Networks (ATHNs), which combine autonomous trucks on middle miles with human-driven trucks on the first and last miles, are seen as the most likely deployment pathway for this technology. This paper presents a framework to optimize ATHN operations and evaluate the benefits of autonomous trucking. By exploiting the problem structure, this paper introduces a flow-based optimization model for this purpose that can be solved by blackbox solvers in a matter of hours. The resulting framework is easy to apply and enables the data-driven analysis of large-scale systems. The power of this approach is demonstrated on a system that spans all of the United States over a four-week horizon. The case study quantifies the potential impact of autonomous trucking and shows that ATHNs can have significant benefits over traditional transportation networks.


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    Titel :

    Optimizing Autonomous Transfer Hub Networks: Quantifying the potential impact of self-driving trucks


    Beteiligte:
    Chungjae Lee (Autor:in) / Kevin Dalmeijer (Autor:in) / Pascal Van Hentenryck (Autor:in) / Peibo Zhang (Autor:in)


    Erscheinungsdatum :

    2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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