Detail publikace

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

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

Jazyk

en

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.

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",
  annote="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.",
  address="Springer Verlag",
  booktitle="Parallel Problem Solving from Nature - PPSN XIII",
  chapter="111559",
  doi="10.1007/978-3-319-10762-2_79",
  edition="Lecture Notes in Computer Science",
  howpublished="print",
  institution="Springer Verlag",
  year="2014",
  month="september",
  pages="802--811",
  publisher="Springer Verlag",
  type="conference paper"
}