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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/15808

Title: Estimating Scalability Issues while Finding an Optimal Assignment for Carpooling
Authors: KNAPEN, Luk
Keren, D.
YASAR, Ansar
CHO, Sungjin
WETS, Geert
Issue Date: 2013
Citation: Procedia Computer Science 19, p. 372-379
Series/Report no.: 19
Abstract: An automatic service to match commuting trips has been designed. Candidate carpoolers register their personal profile and a set of periodically recurring trips. The Global CarPooling Matching Service (GCPMS) shall advise registered candidates on how to combine their commuting trips by carpooling. Planned periodic trips correspond to nodes in a graph; the edges are labeled with the probability for negotiation success while trying to merge planned trips by carpooling. The probability values are calculated by a learning mechanism using on one hand the registered person and trip characteristics and on the other hand the negotiation feedback. The GCPMS provides advice by maximizing the expected value for negotiation success. This paper describes possible ways to determine the optimal advice and estimates computational scalability using real data for Flanders.
URI: http://hdl.handle.net/1942/15808
DOI: 10.1016/j.procs.2013.06.051
ISI #: 000361480500043
ISSN: 1877-0509
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2017
Appears in Collections: Research publications

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