Faculty of Information Technology, BUT

Publication Details

Multiobjective Selection of Input Sensors for Travel Times Forecasting Using Support Vector Regression

PETRLÍK Jiří, FUČÍK Otto and SEKANINA Lukáš. Multiobjective Selection of Input Sensors for Travel Times Forecasting Using Support Vector Regression. In: 2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems Proceedings. Piscataway: Institute of Electrical and Electronics Engineers, 2014, pp. 14-21. ISBN 978-1-4799-4498-9.
Czech title
Výběr vhodných vstupních senzorů pro predikci dojezdových dob s využitím support vector regression
Type
conference paper
Language
english
Authors
Keywords
travel times forecasting, support vector regression, feature selection, multiobjective genetic algorithm
Abstract
In this paper we propose a new method for travel time prediction using a support vector regression model (SVR). The inputs of the method are data from license plate detection systems and traffic sensors such as induction loops or radars placed in the area. This method is mainly designed to be capable of dealing with missing values in traffic data. It is able to create many different SVR models with different input variables. These models are dynamicaly switched according to which traffic variables are currently available. The proposed method was compared with a license plate based prediction approach. The results showed that the proposed method provides a prediction of better quality. Moreover, it is available for a longer period of time.
Published
2014
Pages
14-21
Proceedings
2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems Proceedings
Conference
IEEE Symposium Series on Computational Intelligence, Orlando, US
ISBN
978-1-4799-4498-9
Publisher
Institute of Electrical and Electronics Engineers
Place
Piscataway, US
DOI
BibTeX
@INPROCEEDINGS{FITPUB10684,
   author = "Ji\v{r}\'{i} Petrl\'{i}k and Otto Fu\v{c}\'{i}k and Luk\'{a}\v{s} Sekanina",
   title = "Multiobjective Selection of Input Sensors for Travel Times Forecasting Using Support Vector Regression",
   pages = "14--21",
   booktitle = "2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems Proceedings",
   year = 2014,
   location = "Piscataway, US",
   publisher = "Institute of Electrical and Electronics Engineers",
   ISBN = "978-1-4799-4498-9",
   doi = "10.1109/CIVTS.2014.7009472",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/10684"
}
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