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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F14%3APU111964" target="_blank" >RIV/00216305:26230/14:PU111964 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-10762-2_79" target="_blank" >http://dx.doi.org/10.1007/978-3-319-10762-2_79</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-10762-2_79" target="_blank" >10.1007/978-3-319-10762-2_79</a>

Result language
angličtina
Original language name
Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction
Original language 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.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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