Feature Selection Issues in Long-Term Travel Time Prediction

Conference paper (2016)

Authors

S.M. Hassan NEC Laboratories Europe

L.A. Moreira Matias NEC Laboratories Europe

J Khiari NEC Laboratories Europe

O. Cats Transport and Planning -

Research Group

Transport and Planning () (TU Delft)

Machine learning Feature selection Regression Travel time prediction

To reference this document use:

http://resolver.tudelft.nl/uuid:e9354f27-d742-404c-83c5-8cf4b07c3d52

More Info

expand_more

Published Date

2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Civil Engineering & Geosciences

Department

Transport and Planning

Research Group

Transport and Planning

Abstract

Long-term travel time predictions are crucial for tactical and operational public transport planning in schedule design and resource allocation tasks. Similarly to any regression task, its success considerably depend on an adequate feature selection framework. In this paper, we approach the myopia of the State-of-the-Art method RReliefF on mining relevant inter-relationships of the feature space relevant for reducing the entropy around the target variable on regression tasks. A comparative study was conducted using baseline regression methods and LASSO as a valid alternative to RReliefF. Experimental results obtained on a real-world case study uncovered the bias/variance reduction obtained by each approach, pointing out promising ideas on this research line.