Towards Robust and Accurate Traffic Prediction Using Parallel Multiobjective Genetic Algorithms and Support Vector Regression

The support vector regression (SVR) is a very successful method in solving many difficult tasks in the area of traffic prediction. However, the performance of SVR is very sensitive to the parameters setting and the selection of input variables such as sensors providing the input data. In this paper, the authors describe a new method, which simultaneously optimizes the meta-parameters of SVR model and the subset of its input variables. The method is based on a multiobjective genetic algorithm. The proposed implementation is intended for a parallel environment supporting OpenMP. The authors evaluated the method in the tasks of data imputation, short term prediction of traffic variables and travel times prediction using real world open data. It was confirmed that the simultaneous optimization of SVR parameters and input variables provides better quality of prediction than previous methods.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 2231-2236
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01600682
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:23PM