Prof. Ing. Lukáš Sekanina, Ph.D.

PETRLÍK Jiří and SEKANINA Lukáš. Towards Robust and Accurate Traffic Prediction Using Parallel Multiobjective Genetic Algorithms and Support Vector Regression. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems. Los Alamitos: IEEE Computer Society, 2015, pp. 2231-2236. ISBN 978-1-4673-6596-3.
Publication language:english
Original title:Towards Robust and Accurate Traffic Prediction Using Parallel Multiobjective Genetic Algorithms and Support Vector Regression
Title (cs):Zlepšení kvality a přesnosti dopravní predikce s použitím paralelního vícekriteriálního genetického algoritmu a support vector regression
Pages:2231-2236
Proceedings:2015 IEEE 18th International Conference on Intelligent Transportation Systems
Conference:18th IEEE International Conference on Intelligent Transportation Systems
Place:Los Alamitos, US
Year:2015
ISBN:978-1-4673-6596-3
Publisher:IEEE Computer Society
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Keywords
road traffic forecasting, travel times, support vector regression, multiobjective genetic algorithm
Annotation
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.
BibTeX:
@INPROCEEDINGS{
   author = {Ji{\v{r}}{\'{i}} Petrl{\'{i}}k and Luk{\'{a}}{\v{s}}
	Sekanina},
   title = {Towards Robust and Accurate Traffic Prediction Using
	Parallel Multiobjective Genetic Algorithms and Support
	Vector Regression},
   pages = {2231--2236},
   booktitle = {2015 IEEE 18th International Conference on Intelligent
	Transportation Systems},
   year = {2015},
   location = {Los Alamitos, US},
   publisher = {IEEE Computer Society},
   ISBN = {978-1-4673-6596-3},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=10886}
}

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