Exploiting graph-theoretic tools for matching in carpooling applications

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

doi:10.1007/s12652-013-0197-4

Exploiting graph-theoretic tools for matching in carpooling applications

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

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	393-407
Number of pages	15
Journal	Journal of Ambient Intelligence and Humanized Computing
Volume	5
Issue number	3
DOIs	https://doi.org/10.1007/s12652-013-0197-4
State	Published - May 2014

Bibliographical note

Funding Information:
Acknowledgments The research leading tot these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Nr 270833.

Keywords

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

ASJC Scopus subject areas

General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s12652-013-0197-4

Cite this

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title = "Exploiting graph-theoretic tools for matching in carpooling applications",

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.",

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author = "Luk Knapen and Ansar Yasar and Sungjin Cho and Daniel Keren and Dbai, {Abed Abu} and Tom Bellemans and Davy Janssens and Geert Wets and Assaf Schuster and Izchak Sharfman and Kanishka Bhaduri",

note = "Funding Information: Acknowledgments The research leading tot these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Nr 270833.",

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doi = "10.1007/s12652-013-0197-4",

language = "English",

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AU - Keren, Daniel

AU - Dbai, Abed Abu

AU - Bellemans, Tom

AU - Janssens, Davy

AU - Wets, Geert

AU - Schuster, Assaf

AU - Sharfman, Izchak

AU - Bhaduri, Kanishka

N1 - Funding Information: Acknowledgments The research leading tot these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Nr 270833.

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N2 - 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.

AB - 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.

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Exploiting graph-theoretic tools for matching in carpooling applications

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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