Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Treiber, M., Kesting, A., Thiemann, C.: Traffic Flow Dynamics: Data, Models and Simulation. Springer (2012)
Dia, H.: An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research 131(2), 253–261 (2001)
Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Information Processing-Letters and Reviews 11(10), 203–224 (2007)
Castro-Neto, M., Jeong, Y.-S., Jeong, M.-K., Han, L.D.: Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications 36(3, pt. 2), 6164–6173 (2009)
Hong, W.-C.: Traffic flow forecasting by seasonal svr with chaotic simulated annealing algorithm. Neurocomputing 74(12-13), 2096–2107 (2011)
Li, M.-W., Hong, W.-C., Kang, H.-G.: Urban traffic flow forecasting using gausssvr with cat mapping, cloud model and pso hybrid algorithm. Neurocomputing 99(1), 230–240 (2013)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley Interscience Series in Systems and Optimization. Wiley (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K., Reddy, A.R.: Reliable classification of two-class cancer data using evolutionary algorithms. Biosystems 72(1-2), 111–129 (2003)
Marsland, S.: Machine Learning: An Algorithmic Perspective. CRC (2009)
University of Washington Transportation Research Center, Research Data Exchange Website, Seattle Data Environment, datasets: Arterial Travel Times, www.its-rde.net (retrieved May 2013)
R Core Team: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Petrlik, J., Fucik, O., Sekanina, L. (2014). Multiobjective Selection of Input Sensors for SVR Applied to Road Traffic Prediction. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_79
Download citation
DOI: https://doi.org/10.1007/978-3-319-10762-2_79
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10761-5
Online ISBN: 978-3-319-10762-2
eBook Packages: Computer ScienceComputer Science (R0)