Result Details
Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction
Fučík Otto, doc. Dr. Ing., DCSY (FIT)
Sekanina Lukáš, prof. Ing., Ph.D., DCSY (FIT)
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.
road traffic forecasting, multiobjective feature selection, multiobjective genetic algorithms
@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"
}Centrum excelence IT4Innovations, MŠMT, Operační program Výzkum a vývoj pro inovace, ED1.1.00/02.0070, start: 2011-01-01, end: 2015-12-31, completed
Verification of the implementation of continuous traffic load map using modern classification and prediction methods, TAČR, Program aplikovaného výzkumu a experimentálního vývoje ALFA, TA02030915, start: 2012-01-01, end: 2014-12-31, completed