Computer Science ›› 2019, Vol. 46 ›› Issue (11): 241-246.doi: 10.11896/jsjkx.191100507C

• Artificial Intelligence • Previous Articles     Next Articles

Traffic Congestion Prediction Based on Kernel Extreme Learning Machine Group Algorithm

XING Yi-ming1,2, BAN Xiao-juan1, LIU Xu1, YIN Hang2, SHEN Qing1   

  1. (School of Computer and Communication Engineering,University of Science and Technology,Beijing 100086,China)1
    (Engineering Training Center,Shenyang Aerospace University,Shenyang 110136,China)2
  • Received:2018-11-07 Online:2019-11-15 Published:2019-11-14

Abstract: Prediction of urban traffic congestion is one of the important research contents of intelligent transportation system (ITS).At present,a lot of neural networks are introduced into the field of traffic forecasting and are widely used.However,the traditional neural network training is time-consuming,easy to fall into local optimal and over fitting.It has seriously hindered the large-scale application of neural network in the field of traffic forecasting.ELM is a new kind of single hidden layer feed-forward neural network,which has the advantages of fast training spead,stronggenera-lization ability and unique optimal solution.In this paper,the new algorithm named KELM-Group was proposed,which is composed of multiple KELM sub-models.KELM-Group algorithm enables each class of samples to achieve the global optimum,and the overall prediction accuracy can be higher than that of ELM.The experimental results show that the KELM-Group algorithm is faster than other popular machine learning algorithms.The accuracy rate of KELM-Group algorithm is 8% higher than that of the ELM.The results predicted by the KELM-Group algorithm are more consistent with the actual situation,and have great practical value.

Key words: Group, Kernel extreme learning machine, Neural network, Prediction, Traffic congestion

CLC Number: 

  • TP399
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