Detail publikace

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

PETRLÍK, J. SEKANINA, L.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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, we 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. We 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.

Klíčová slova

road traffic forecasting, travel times, support vector regression, multiobjective genetic algorithm

Autoři

PETRLÍK, J.; SEKANINA, L.

Rok RIV

2015

Vydáno

15. 9. 2015

Nakladatel

IEEE Computer Society

Místo

Los Alamitos

ISBN

978-1-4673-6596-3

Kniha

2015 IEEE 18th International Conference on Intelligent Transportation Systems

Strany od

2231

Strany do

2236

Strany počet

6

URL

https://www.fit.vut.cz/research/publication/10886/

BibTex

@inproceedings{BUT119857,
  author="Jiří {Petrlík} and Lukáš {Sekanina}",
  title="Towards Robust and Accurate Traffic Prediction Using Parallel Multiobjective Genetic Algorithms and Support Vector Regression",
  booktitle="2015 IEEE 18th International Conference on Intelligent Transportation Systems",
  year="2015",
  pages="2231--2236",
  publisher="IEEE Computer Society",
  address="Los Alamitos",
  doi="10.1109/ITSC.2015.360",
  isbn="978-1-4673-6596-3",
  url="https://www.fit.vut.cz/research/publication/10886/"
}