On recommending urban hotspots to find our next passenger

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013

Autores

Luís Moreira-Matias (Autor) FEUP; Ricardo Fernandes (Autor) Outra; João Gama (Autor) FEP; Michel Ferreira (Autor) FCUP; João Mendes-Moreira (Autor) FEUP; Luís Damas (Autor) Outra

Ata de Conferência Internacional

Título: CEUR Workshop Proceedings

Páginas: 17-23

3rd Workshop on Ubiquitous Data Mining, UDM 2013 - Co-located with the 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

3 August 2013

Indexação

Scopus - 4 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Físicas > Ciência de computadores > Cibernética > Inteligência artificial

Outras Informações

ID Authenticus: P-008-KWV

Abstract (EN): The rising fuel costs is disallowing random cruising strategies for passenger finding. Hereby, a recommendation model to suggest the most passengerprofitable urban area/stand is presented. This framework is able to combine the 1) underlying historical patterns on passenger demand and the 2) current network status to decide which is the best zone to head to in each moment. The major contribution of this work is on how to combine well-known methods for learning from data streams (such as the historical GPS traces) as an approach to solve this particular problem. The results were promising: 395.361/506.873 of the services dispatched were correctly predicted. The experiments also highlighted that a fleet equipped with such framework surpassed a fleet that is not: they experienced an average waiting time to pick-up a passenger 5% lower than its competitor. © 2013 IJCAI.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 1