You are here

ICON Loop Carpooling Show Case

Authors: 

Mirco Nanni, Lars Kotthoff, Riccardo Guidotti, Barry O'Sullivan, Dino Pedreschi

Publication Type: 
Refereed Original Article
Abstract: 
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.
Digital Object Identifer (DOI): 
10.1007/978-3-319-50137-6_13
Publication Status: 
Published
Publication Date: 
03/12/2016
Journal: 
Lecture Notes in Computer Science
Volume: 
10101
Pages: 
310-324
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
No