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

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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. 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. (en)
Title	Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction (en)
skos:prefLabel	Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction (en)
skos:notation	RIV/00216305:26230/14:PU111964!RIV15-MSM-26230___
http://linked.open...avai/riv/aktivita	P S
http://linked.open...avai/riv/aktivity	P(ED1.1.00/02.0070), P(TA02030915), S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Fučík, Otto Sekanina, Lukáš Petrlík, Jiří
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoké učení technické v Brně / Fakulta informačních technologií
http://linked.open...dnocenehoVysledku	31064
http://linked.open...ai/riv/idVysledku	RIV/00216305:26230/14:PU111964
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	road traffic forecasting, multiobjective feature selection, multiobjective genetic algorithms (en)
http://linked.open.../riv/klicoveSlovo	multiobjective feature selection multiobjective genetic algorithms road traffic forecasting
http://linked.open...ontrolniKodProRIV	[49D5F416F897]
http://linked.open...v/mistoKonaniAkce	Ljubljana Exhibition and Convention Centre
http://linked.open...i/riv/mistoVydani	Heidelberg
http://linked.open...i/riv/nazevZdroje	Parallel Problem Solving from Nature - PPSN XIII
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Verification of the implementation of continuous traffic load map using modern classification and prediction methods IT4Innovations Centre of Excellence
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Fučík, Otto Sekanina, Lukáš Petrlík, Jiří
http://linked.open...vavai/riv/typAkce	CST - Celostátní
http://linked.open.../riv/zahajeniAkce	2014-09-13 (xsd:date)
number of pages	10 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-319-10762-2_79
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-319-10761-5
http://localhost/t...ganizacniJednotka	26230

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