Document - ICON loop carpooling show case

2016

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ICON loop carpooling show case

Nanni M., Kotthoff L., Guidotti R., Òsullivan B., Pedreschi D.

Mobility Profiles Constraint Programming Carpooling

In 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.

Source: Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach, edited by Bessiere, C.; De Raedt, L.; Kotthoff, L.; Nijssen, S.; O'Sullivan, B.; Pedreschi, D., pp. 310–324, 2016

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@inbook{oai:it.cnr:prodotti:366906,
	title = {ICON loop carpooling show case},
	author = {Nanni M. and Kotthoff L. and Guidotti R. and Òsullivan B. and Pedreschi D.},
	doi = {10.1007/978-3-319-50137-6_13},
	booktitle = {Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach, edited by Bessiere, C.; De Raedt, L.; Kotthoff, L.; Nijssen, S.; O'Sullivan, B.; Pedreschi, D., pp. 310–324, 2016},
	year = {2016}
}