Conference paper

PETRLÍK Jiří, FUČÍK Otto and SEKANINA Lukáš. Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction. In: Parallel Problem Solving from Nature - PPSN XIII. Heidelberg: Springer Verlag, 2014, pp. 802-811. ISBN 978-3-319-10761-5.
Publication language:english
Original title:Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction
Title (cs):Multikriteriální výběr vstupních senzorů pro SVR pro účely predikce dopravy
Proceedings:Parallel Problem Solving from Nature - PPSN XIII
Conference:Parallel Problem Solving from Nature 2014
Series:Lecture Notes in Computer Science, 8672
Place:Heidelberg, DE
Publisher:Springer Verlag
road traffic forecasting, multiobjective feature selection, multiobjective genetic algorithms
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.
   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 SVR Applied to
	Road Traffic Prediction},
   pages = {802--811},
   booktitle = {Parallel Problem Solving from Nature - PPSN XIII},
   series = {Lecture Notes in Computer Science, 8672},
   year = {2014},
   location = {Heidelberg, DE},
   publisher = {Springer Verlag},
   ISBN = {978-3-319-10761-5},
   language = {english},
   url = {}

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