On recommending urban hotspots to find our next passenger

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

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

Conference proceedings International

Title: CEUR Workshop Proceedings

Pages: 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

Indexing

Scopus - 4 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 1