Detail publikace

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

PETRLÍK, J. FUČÍK, O. SEKANINA, L.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

travel times forecasting, support vector regression, feature selection, multiobjective genetic algorithm

Autoři

PETRLÍK, J.; FUČÍK, O.; SEKANINA, L.

Rok RIV

2014

Vydáno

12. 12. 2014

Nakladatel

Institute of Electrical and Electronics Engineers

Místo

Piscataway

ISBN

978-1-4799-4498-9

Kniha

2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems Proceedings

Strany od

14

Strany do

21

Strany počet

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"
}