Trip time prediction in mass transit companies. A machine learning approach

• Type: Article in International Conference Proceedings Book
• Date: 2005

Authors

João M. Moreira (Author) FEUP; Alípio Jorge (Author) FEUP; Jorge Freire de Sousa (Author) FEUP; Carlos Soares (Author) FEUP

Conference proceedings International

Title: Advanced OR and AI methods in transportation

Pages: 276-283

EURO working group in transportation

Poznan, 13-16 Septembet 2005

Scientific classification

CORDIS: Technological sciences > Engineering

Other information

Abstract (EN): In this paper we discuss how trip time prediction can be useful for operational optimization in mass transit companies and which machine learning techniques can be used to improve results. Firstly, we analyze which departments need trip time prediction and when. Secondly, we review related work and thirdly we present the analysis of trip time over a particular path. We proceed by presenting experimental results conducted on real data with the forecasting techniques we found most adequate, and conclude by discussing guidelines for future work.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8