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                                       Details for article 3 of 10 found articles
 
 
  Comparing state-of-the-art regression methods for long term travel time prediction
 
 
Title: Comparing state-of-the-art regression methods for long term travel time prediction
Author: Mendes-Moreira, João
Jorge, Alípio Mário
de Sousa, Jorge Freire
Soares, Carlos
Appeared in: Intelligent data analysis
Paging: Volume 16 (2012) nr. 3 pages 427-449
Year: 2012-05-09
Contents: 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. From an off-the-shelf point-of-view, our experiments show that RF is the most promising approach from the three we have tested. However, it is possible to obtain more accurate results using PPR but with extra pre-processing work, namely on example selection and domain values definition.
Publisher: IOS Press
Source file: Elektronische Wetenschappelijke Tijdschriften
 
 

                             Details for article 3 of 10 found articles
 
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