Comparing state-of-the-art regression methods for long term travel time prediction
Comparing state-of-the-art regression methods for long term travel time prediction
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Date
2012
Authors
João Mendes Moreira
Jorge Freire de Sousa
Alípio Jorge
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Abstract
Long-term travel time prediction (TTP) can be an important planning tool for both freight transport and public
transport companies. In both cases it is expected that the use of long-term TTP can improve the quality of the planned services
by reducing the error between the actual and the planned travel times. However, for reasons that we try to stretch out along
this paper, long-term TTP is almost not mentioned in the scientific literature. In this paper we discuss the relevance of this
study and compare three non-parametric state-of-the-art regression methods: Projection Pursuit Regression (PPR), Support
Vector Machine (SVM) and Random Forests (RF). For each one of these methods we study the best combination of input
parameters. We also study the impact of different methods for the pre-processing tasks (feature selection, example selection
and domain values definition) in the accuracy of those algorithms. We use bus travel time's data from a bus dispatch system.
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