Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction
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.
KeywordsRoad traffic forecasting multiobjective feature selection multiobjective genetic algorithms
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- 1.Treiber, M., Kesting, A., Thiemann, C.: Traffic Flow Dynamics: Data, Models and Simulation. Springer (2012)Google Scholar
- 3.Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Information Processing-Letters and Reviews 11(10), 203–224 (2007)Google Scholar
- 7.Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley Interscience Series in Systems and Optimization. Wiley (2001)Google Scholar
- 10.Marsland, S.: Machine Learning: An Algorithmic Perspective. CRC (2009)Google Scholar
- 11.University of Washington Transportation Research Center, Research Data Exchange Website, Seattle Data Environment, datasets: Arterial Travel Times, www.its-rde.net (retrieved May 2013)
- 12.R Core Team: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria (2013)Google Scholar