01445nas a2200169   4500008004100000245003500041210003500076300000800111490001000119520096400129100001701093700001901110700002301129700002201152700002001174856008101194       2017                        eng d00aICON Loop Carpooling Show Case0 aICON Loop Carpooling Show Case  a3100 v101013 aIn this chapter we describe a proactive carpooling service that combines induction and optimization mechanisms to maximize the impact of carpooling within a community. The approach autonomously infers the mobility demand of the users through the analysis of their mobility traces (i.e. Data Mining of GPS trajectories) and builds the network of all possible ride sharing opportunities among the users. Then, the maximal set of carpooling matches that satisfy some standard requirements (maximal capacity of vehicles, etc.) is computed through Constraint Programming models, and the resulting matches are proactively proposed to the users. Finally, in order to maximize the expected impact of the service, the probability that each carpooling match is accepted by the users involved is inferred through Machine Learning mechanisms and put in the CP model. The whole process is reiterated at regular intervals, thus forming an instance of the general ICON loop.1 aNanni, Mirco1 aKotthoff, Lars1 aGuidotti, Riccardo1 aO'Sullivan, Barry1 aPedreschi, Dino  uhttps://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=314