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

• Author(s): João M. Moreira ; Alípio Jorge ; Jorge Freire de Sousa ; Carlos Soares
• Date: 2005
• Persistent ID: http://hdl.handle.net/10216/6749
• Origin: Repositório Aberto da Universidade do Porto
• Subject(s): Ciências tecnológicas; Engenharia
• Document Type: Conference Object
• Language: Portuguese

Description

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.