Smart phones and social networking tools allow to collect large-scale data about mobility habits of people. These data can support advanced forms of sharing, coordination and cooperation possibly able to reduce the overall demand for mobility. We present a methodology, based on the extraction of suitable information from mobility traces, to identify rides along the same trajectories that are amenable for ride sharing. Results on a real dataset show that, assuming users are willing to share rides and tolerate 1Km detours, about 60% of trips could be saved.


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    Titel :

    Opportunistic Ride Sharing via Whereabouts Analysis


    Beteiligte:
    Bicocchi, Nicola (Autor:in) / Mamei, Marco (Autor:in) / Sassi, Andrea (Autor:in) / Zambonelli, Franco (Autor:in)


    Erscheinungsdatum :

    2015-09-01


    Format / Umfang :

    537992 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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