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PETRLÍK, J. FUČÍK, O. SEKANINA, L.
Original Title
Multiobjective Selection of Input Sensors for Travel Times Forecasting Using Support Vector Regression
Type
conference paper
Language
English
Original 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.
Keywords
travel times forecasting, support vector regression, feature selection, multiobjective genetic algorithm
Authors
PETRLÍK, J.; FUČÍK, O.; SEKANINA, L.
RIV year
2014
Released
12. 12. 2014
Publisher
Institute of Electrical and Electronics Engineers
Location
Piscataway
ISBN
978-1-4799-4498-9
Book
2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems Proceedings
Pages from
14
Pages to
21
Pages count
8
BibTex
@inproceedings{BUT111629, author="Jiří {Petrlík} and Otto {Fučík} and Lukáš {Sekanina}", title="Multiobjective Selection of Input Sensors for Travel Times Forecasting Using Support Vector Regression", booktitle="2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems Proceedings", year="2014", pages="14--21", publisher="Institute of Electrical and Electronics Engineers", address="Piscataway", doi="10.1109/CIVTS.2014.7009472", isbn="978-1-4799-4498-9" }