On Predicting the Taxi-Passenger Demand: A Real-Time Approach

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

Authors

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

Conference proceedings International

Title: PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2013

Pages: 54-65

16th Portuguese Conference on Artificial Intelligence (EPIA)

Angra do Heroismo, PORTUGAL, SEP 09-12, 2013

Indexing

ISI Web of Knowledge - 33 Citations

Scopus - 43 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

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

Other information

Authenticus ID: P-008-EFG

DOI: 10.1007/978-3-642-40669-0_6

Abstract (EN): Informed driving is becoming a key feature to increase the sustainability of taxi companies. Some recent works are exploring the data broadcasted by each vehicle to provide live information for decision making. In this paper, we propose a method to employ a learning model based on historical GPS data in a real-time environment. Our goal is to predict the spatiotemporal distribution of the Taxi-Passenger demand in a short time horizon. We did so by using learning concepts originally proposed to a well-known online algorithm: the perceptron [1]. The results were promising: we accomplished a satisfactory performance to output the next prediction using a short amount of resources.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12