Affiliations: [a] Departamento de Engenharia Informática, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal | [b] Faculdade de Ciencias, Universidade do Porto, Porto, Portugal | [c] Departamento de Engenharia Industrial e Gestão, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal | [d] Faculdade de Economia, Universidade do Porto, Porto, Portugal | [e] LIAAD-INESC TEC, Porto, Portugal | [f] UGEI-INESC TEC, Porto, Portugal | [g] UESP-INESC TEC, Porto, Portugal
Correspondence:
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Corresponding author: João Mendes-Moreira, Departamento de Engenharia Informática, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal. E-mail: [email protected]
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. 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.
Keywords: Travel time prediction, long-term, machine learning, regression
