Exploiting graph-theoretic tools for matching in carpooling applications

Authored by Luk Knapen, Davy Janssens, Geert Wets, Ansar Yasar, Sungjin Cho, Daniel Keren, Abed Abu Dbai, Tom Bellemans, Assaf Schuster, Izchak Sharfman, Kanishka Bhaduri

Date Published: 2014-06

DOI: 10.1007/s12652-013-0197-4

Sponsors: European Union

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts

Model Code URLs: Model code not found

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 shall advise registered candidates 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 for success while negotiating to merge two 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 probability values vary over time due to repetitive execution of the learning mechanism. As a consequence, the matcher needs to cope with a dynamically changing graph both with respect to topology and edge weights. In order to evaluate the matcher performance before deployment in the real world, it will be exercised using a large scale agent based model. This paper describes both the exercising model and the matcher.

Tags

Agent-based modeling Learning dynamic networks Binary matching Graph theory Scalability