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An ensemble regression approach for bus trip time prediction

This paper is about bus trip time prediction in mass transit companies. We describe the motivations to accomplish this task and how it can support operational management on such companies. Then, we describe a Data Mining framework that recommends the expected best regression algorithm(s), from an ensemble, to predict the duration of a given trip. We present results that show the advantage of using an ensemble r...

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

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...

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

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 cond...

Avaliação de Interfaces no Transporte de Passageiros

Planning and control indicators for mass transit companies

The Planning Indicators Board (PIB) is a top level oriented module for mass transit companies. The proposal is to collect and to show the planning and control information to support the decision making process. It uses Data Warehousing technology.

Performance indicators board

The Performance Indicators Board (PIB) is a top level oriented module for mass transport companies. The objective of PIB is to collect and to show the planning and control information to support the decision making process. In this paper, the authors desc