Estimating Scalability Issues While Finding an Optimal Assignment for Carpooling | Zendy

Luk Knapen | Zendy; Daniel Keren | Zendy; Ansar-Ul-Haque Yasar | Zendy; SungJin Cho | Zendy; Tom Bellemans | Zendy; Davy Janssens | Zendy; Geert Wets | Zendy

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Estimating Scalability Issues While Finding an Optimal Assignment for Carpooling

Author(s) -

Luk Knapen,

Daniel Keren,

Ansar-Ul-Haque Yasar,

SungJin Cho,

Tom Bellemans,

Davy Janssens,

Geert Wets

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.06.051

Subject(s) - trips architecture , computer science , merge (version control) , negotiation , scalability , matching (statistics) , service (business) , set (abstract data type) , operations research , information retrieval , database , marketing , mathematics , statistics , business , parallel computing , political science , law , programming language

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

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