Journal of Applied Science and Engineering

Published by Tamkang University Press


Impact Factor



Yuchi Zhang1 and Zhixiang Hou This email address is being protected from spambots. You need JavaScript enabled to view it.2

1Hunan Industry Polytechnic, Changsha, Hunan 410082, P.R. China
2College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, P.R. China


Received: November 17, 2016
Accepted: April 13, 2017
Publication Date: March 1, 2018

Download Citation: ||  


The traditional forecasting methods are not suitable for short term traffic flow prediction, due to strong non-linear, time varying characteristics of urban transportation system. In order to improve forecasting accuracy of short term traffic flow, short term traffic flow prediction model based on support vector machine is presented. The most important parameter of support vector machine is parameter selection including the kernel function parameter and the penalty factor, which has significant influence on the properties of model prediction. Particle swarm optimization is used to optimize support vector machine, and particle swarm optimization is improved by means of adjusting inertia weight and choosing acceleration constant dynamically. Then improved particle swarm optimization is used to optimize support vector parameter. The experiment results show that predictive result based on improved particle swarm optimization LSSVM is closer to the real traffic flow data compared with support vector machine based on basic particle swarm optimization. Short term traffic prediction model based on improved particle swarm optimized support vector machine is feasible.

Keywords: Support Vector Machine, Particle Swarm Optimization, Traffic Flow Prediction, Parameter Selection


  1. [1] Manoel, C. N., Jeong, Y. S., Jeong, M. K. and Han, L. D., “Online-SVR for Short-term Traffic Flow Prediction under Typical and Atypical Traffic Conditions,” Expert Systems with Applications, Vol. 36, pp. 6164 6173 (2009). doi: 10.1016/j.eswa.2008.07.069
  2. [2] Chan, K. Y., Dillon, T. S., Singh., J., et al., “Neural network-based Models for Short-term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg-marquardt Algorithm,” IEEE Transactions on Intelligent Transportation Systems, Vol. 13, No. 2, pp. 644654 (2012). doi: 10.1109/TITS.2011.2174051
  3. [3] Yan, X., Zhang, X., Kuang, Q. J. and Liu, Y. C., “Highway Traffic Flow Short-term Forecasting Method,” Journal of Transportation Engineering, Vol. 13, No. 2, pp. 114119 (2013).
  4. [4] Yang, J. and Fu, B. Q., “Urban Road Short-term Traffic Flow Forecasting VHSSAModel,” Journal of Highway and Transportation Research and Development, Vol. 31, No. 5, pp. 135146 (2014).
  5. [5] Zhang, H. B., Sun, X. D. and Hu, Y. L., “Complex Dynamic Characteristics Analysis and Prediction on Short-term Traffic Flow,” Journal of Physics, Vol. 63, No. 4, pp. 18 (2014).
  6. [6] Marco, L., Matteo, B. and Paolo, F., “Short-term Traffic Flow Forecasting: an Experimental Comparison of Time-series Analysis and Supervised Leaming,” IEEE Transaction on Intelligent Transportation Systems, Vol. 14, No. 2, pp. 871882 (2013). doi: 10.1109/TITS.2013.2247040
  7. [7] Sun, Z. Q. and Fox, G., “Traffic Flow Forecasting Based on Combination of Multidimensional Scaling and SVM,” International Journal of Intelligent Transportation Systems Research, Vol. 12, No. 1, pp. 2025 (2014). doi: 10.1007/s13177-013-0065-9
  8. [8] Xing, Y. and Nurul, A. C., “Mid-term Electricity Market Clearing Price Forecasting: a Hybrid LSSVM and ARMAX Approach,” International Journal of Electrical Power and Energy Systems, Vol. 53, No. 1, pp.2026 (2013). doi: 10.1016/j.ijepes.2013.04.006
  9. [9] Zhang, C. Y. and Li, C., “Improved LS-SVM Algorithm and the Application in Traffic Flow Prediction,” Journal of Kunming University of Science and Technology, Vol. 33, No. 6, pp. 7275 (2008). (Chinese)
  10. [10] Tang, J. J., Xu, G. N. and Wang, Y. H., “Traffic Flow Prediction Based on Hybrid Model Using Double Exponential Smoothing and Support Vector Machine,” 16th International IEEE Conference on Intelligent Transportation Systems, pp. 130135 (2013). doi: 10.1109/ITSC.2013.6728222
  11. [11] Tang, X. L., Li, C. G. and Wang, M., “Traffic Flow Forecasting Based on the Wavelet Neural Network with Particle Swarm Optimization Algorithm,” Computer Measurement and Control, Vol. 8, pp. 1893 1895 (2010). (Chinese)
  12. [12] Ye, Y. and Lv, Z. L., “Prediction Based on Particle Swarm Optimization Neural Network for Short-term Traffic Flow,” Computer Engineering and Design, Vol.30, No. 18, pp. 42964298 (2009). (Chinese)
  13. [13] Lin, W. M., Tu, C. S., Yang, R. F. and Tsai, M. T., “Particle Swarm OptimizationAided Least-square Support Vector Machine for Load Forecast with Spikes,” IET Generation, Transmission &Distribution, Vol. 10, No.5, pp. 11451153 (2016). doi: 10.1049/iet-gtd.2015.0702
  14. [14] Yu, L., Chen, H. H., Wang, S. Y. and Lai, K. K., “Evolving Least Squares Support Vector Machines for Stock Market Trend Mining,” IEEE Transactions on Evolutionary Computation, Vol. 13, No. 1, pp. 87102 (2009). doi: 10.1109/TEVC.2008.928176
  15. [15] Brabanter, K. D., Brabanter, J. D., Suykens, J. A. K. and Moor, B. D., “Optimized Fixed-size Kernel Models for Large Data Sets,” Comput. Statist. Data Anal., Vol. 54, No. 6, pp. 14841504 (2010). doi: 10.1016/j.csda.2010.01.024
  16. [16] Ismail, S., Shabri, A. and Samsudin, R., “A Hybrid Model of Self-organizing Maps (SOM) and Least Square Support Vector Machine (LSSVM) for Time series Forecasting,” Expert Systems with Applications, Vol. 38. No. 8, pp. 1057410578 (2011). doi: 10.1016/j.eswa.2011.02.107
  17. [17] Su, H., “Chaos Quantum-behaved Particle Swarm Optimization Based Neural Networks for Short-term Load Forecasting,” Procedia Engineering, Vol. 15, pp. 199 203 (2011). doi: 10.1016/j.proeng.2011.08.040
  18. [18] Zheng, B. and Cui, B. T., “A Routing Protocol for WSN Based on Chaotic PSO and Ant Colony Algorithm,” Electronic Design Engineering, Vol. 45, No. 5, pp. 706715 (2015).
  19. [19] Fong, S., Wong, R., et al., “Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data,” IEEE Transactions on Services Computing, Vol. 9, No. 1, pp. 3345 (2015). doi: 10.1109/TSC.2015.2439695
  20. [20] Shi, Y. and Eberhart, R. C. “A Modified Particle Swarm Optimizer,” The Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, Anchorage, Anchorage, AK, USA, USA.
  21. [21] Wei, X. Y. and Pan, H. X., Particle Swarm Optimization and Intelligent Fault Diagnosis, National Defense Industry Press (2010). (Chinese)