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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2009  >  9(5): 94-98
LIU Zhao-hui. Multifactor prediction model for traffic accident based on grey-radial basis function neural network[J]. Journal of Traffic and Transportation Engineering, 2009, 9(5): 94-98. doi: 10.19818/j.cnki.1671-1637.2009.05.017
Citation: LIU Zhao-hui. Multifactor prediction model for traffic accident based on grey-radial basis function neural network[J]. Journal of Traffic and Transportation Engineering, 2009, 9(5): 94-98. doi: 10.19818/j.cnki.1671-1637.2009.05.017
shu

Multifactor prediction model for traffic accident based on grey-radial basis function neural network

doi: 10.19818/j.cnki.1671-1637.2009.05.017

  • School of Resources and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266510, Shandong, China

  • Received Date: 2009-04-18
  • Publish Date: 2009-10-25
    Abstract
  • In order to realize the traffic accident prediction under various road factors, a muhifactor prediction model based on grey-radial basis funetion(RBF) neural network was put forward, which combined grey system theory with neural network theory. Grey theory could improve the utilization of available information and weaken the undulation of data sequence. Neural network had the processing ability of nonlinear adaptability information. The muhifactor prediction model was compared with different prediction methods by a case. Analysis result shows that in comparison with grey prediction and RBF neural network prediction, the mean absolute error and mean absolute percent error of muhifaetor prediction model decrease by 50% and 12.5%, the Theil coefficient decreases by 54.5% and 16.6%, the validity increases by 2.7% and 0.3% respectively, so the combined prediction method can improve the efficiency of system modeling and the precision of prediction model.

     

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