Feature Selection Issues in Long-Term Travel Time Prediction

S.M. Hassan, Luis Moreira Matias, J Khiari, Oded Cats

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

9 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationInternational Symposium on Intelligent Data Analysis
Subtitle of host publicationAdvances in Intelligent Data Analysis XV
EditorsH. Boström, A. Knobbe, C. Soares, P. Papapetrou
PublisherSpringer
Pages98-109
Number of pages12
ISBN (Electronic)978-3-319-46349-0
ISBN (Print)978-3-319-46348-3
DOIs
Publication statusPublished - 2016
EventThe 15th International Symposium on Intelligent Data Analysis - Stockholm, Sweden
Duration: 13 Oct 201615 Oct 2016
Conference number: 15
http://ida2016.blogs.dsv.su.se/

Publication series

NameLecture Notes in Computer Science
Volume9897

Conference

ConferenceThe 15th International Symposium on Intelligent Data Analysis
Abbreviated titleIDA 2016
Country/TerritorySweden
CityStockholm
Period13/10/1615/10/16
Internet address

Keywords

  • Travel time prediction
  • Machine learning
  • Regression
  • Feature selection

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