Detail publikačního výsledku

Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction

PETRLÍK, J.; FUČÍK, O.; SEKANINA, L.

Originální název

Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction

Anglický název

Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Modern traffic sensors can measure various road traffic variables such as the traffic flow and average speed. However, some measurements can lead to incorrect data which cannot  further be used in subsequent processing tasks such as traffic prediction or intelligent control. In this paper, we propose a method selecting a subset of input sensors for a support vector regression (SVR) model which is used for traffic prediction. The method is based on a multimodal and multiobjective NSGA-II algorithm. The multiobjective approach allowed us to find a good trade off between the prediction error and the number of sensors in real-world situations when many traffic data measurements are unavailable.

Anglický abstrakt

Modern traffic sensors can measure various road traffic variables such as the traffic flow and average speed. However, some measurements can lead to incorrect data which cannot  further be used in subsequent processing tasks such as traffic prediction or intelligent control. In this paper, we propose a method selecting a subset of input sensors for a support vector regression (SVR) model which is used for traffic prediction. The method is based on a multimodal and multiobjective NSGA-II algorithm. The multiobjective approach allowed us to find a good trade off between the prediction error and the number of sensors in real-world situations when many traffic data measurements are unavailable.

Klíčová slova

road traffic forecasting, multiobjective feature selection, multiobjective genetic algorithms

Klíčová slova v angličtině

road traffic forecasting, multiobjective feature selection, multiobjective genetic algorithms

Autoři

PETRLÍK, J.; FUČÍK, O.; SEKANINA, L.

Rok RIV

2015

Vydáno

13.09.2014

Nakladatel

Springer Verlag

Místo

Heidelberg

ISBN

978-3-319-10761-5

Kniha

Parallel Problem Solving from Nature - PPSN XIII

Edice

Lecture Notes in Computer Science

Svazek

8672

Strany od

802

Strany do

811

Strany počet

10

BibTex

@inproceedings{BUT111559,
  author="Jiří {Petrlík} and Otto {Fučík} and Lukáš {Sekanina}",
  title="Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction",
  booktitle="Parallel Problem Solving from Nature - PPSN XIII",
  year="2014",
  series="Lecture Notes in Computer Science",
  volume="8672",
  pages="802--811",
  publisher="Springer Verlag",
  address="Heidelberg",
  doi="10.1007/978-3-319-10762-2\{_}79",
  isbn="978-3-319-10761-5"
}