A data mining approach for trip time prediction in mass transit companies

• 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: Proceedings of the workshop W11 on Data Mining for Business

Pages: 63-66

workshop W11 on Data Mining for Business co-located with ECML/PKDD

Porto - Portugal, 3 de Outubro de 2005

Scientific classification

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

CORDIS: Technological sciences > Technology > Transport technology

Other information

Abstract (EN): In this paper we discuss how trip time prediction can be useful for operational optimization in mass transit companies and how data mining techniques can be used to improve results. Firstly, we an- alyze 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: 4