Opportunistic Ride Sharing via Whereabouts Analysis

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.