An Online Learning Framework for Predicting the Taxi Stand's Profitability

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

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

Conference proceedings International

Title: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

Pages: 2009-2014

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00G-DJ2

DOI: 10.1109/itsc.2014.6957999

Abstract (EN): Taxi services play a central role in the mobility dynamics of major urban areas. Advanced communication devices such as GPS (Global Positioning System) and GSM (Global System for Mobile Communications) made it possible to monitor the drivers' activities in real-time. This paper presents an online learning approach to predict profitability in taxi stands. This approach consists of classifying each stand based according to the type of services that are being requested (for instance, short and long trips). This classification is achieved by maintaining a time-evolving histogram to approximate local probability density functions (p.d.f.) in service revenues. The future values of this histogram are estimated using time series analysis methods assuming that a non-homogeneous Poisson process is in place. Finally, the method's outputs were combined using a voting ensemble scheme based on a sliding window of historical data. Experimental tests were conducted using online data transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide an effective insight on the characterization of taxi stand profitability.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6