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• W2606376502 abstract "Research on social and mobile technologies recently provided tools to collect and mine massive amounts of mobility data. Ride sharing is one of the most prominent applications in this area. While a number of research and commercial initiatives already proposed solutions for long-distance journeys, the opportunities provided by modern pervasive systems can be used to promote local, daily ride sharing within the city. We present a set of algorithms to analyze urban mobility traces and to recognize matching rides along similar routes. These rides are amenable for ride sharing recommendations. We validate the proposed methodology using data provided by a large Italian telecom operator. Assuming the full set of considered users are willing to accept 1-km detours, experimental results on two large cities show that more than 60% of trips could be saved. These results can be used to evaluate the potential of a ride sharing system before its actual deployment and to actually support an opportunistic ride sharing recommender system." @default.
• W2606376502 created "2017-04-28" @default.
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• W2606376502 date "2017-12-01" @default.
• W2606376502 modified "2023-10-01" @default.
• W2606376502 title "On Recommending Opportunistic Rides" @default.
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• W2606376502 doi "https://doi.org/10.1109/tits.2017.2684625" @default.
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