Scalability issues in optimal assignment for carpooling

Authored by Luk Knapen, Ansar-Ul-Haque Yasar, Davy Janssens, Geert Wets, Sungjin Cho, Daniel Keren, Tom Bellemans, Irith Ben-Arroyo Hartman

Date Published: 2015

DOI: 10.1016/j.jcss.2014.11.010

Sponsors: European Union

Platforms: No platforms listed

Model Documentation: UML Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

Carpooling for commuting can save cost and helps in reducing pollution. An automatic Web based Global Carpooling Matching Service (GCPMS) for matching commuting trips has been designed. The service supports carpooling candidates by supplying advice during their exploration for potential partners. Such services collect data about the candidates, and base their advice for each pair of trips to be combined, on an estimate of the probability for successful negotiation between the candidates to carpool. 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 problem of maximizing the expected value of carpooling negotiation success was formulated and was proved to be NP-hard. In addition, the network characteristics for a realistic case have been analyzed. The carpooling network was established using results predicted by the operational FEATHERS activity based model for Flanders (Belgium). (C) 2014 Elsevier Inc. All rights reserved.

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