Most of current urban logistics systems do not offer a good compromise between speed and cost. Express logistics services are often associated with high shipping costs. To alleviate such contradiction, crowdsourced logistics is an encouraging solution. In this chapter, we present a new type of crowdsourcing logistics to organize packages and passengers in a shared space, i.e., using a taxi that has already picked up a passenger as a package hitchhiker for on-time delivery. Specifically, for the on-time package express service, we propose a two-stage probabilistic framework named as CrowdExpress. In the first stage, we build a package transport network by mining the historical GPS trajectory data of taxi. In the second stage, we design a taxi scheduling algorithm to dynamically find the path with the maximum probability of arriving on time based on real-time requests sent by passengers, and give the corresponding package routing. Finally, we use the real-world taxi data in the city of New York, US in a month to evaluate the system. The experiment results show that about 9500 packages are delivered successfully daily on time with a success rate of over 94%.


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    Titel :

    CrowdExpress: Making Citywide Packages Arrive as many as Possible


    Beteiligte:
    Chen, Chao (Autor:in) / Zhang, Daqing (Autor:in) / Wang, Yasha (Autor:in) / Huang, Hongyu (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    02.04.2021


    Format / Umfang :

    28 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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