A visual automatic incident detection method on freeway based on RBF and SOFM neural networks

  • Authors:

  • Xuhua Yang;Qiu Guan;Wanliang Wang;Shengyong Chen

  • Affiliations:

  • College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper proposes a novel visual automatic incident detection method on freeway based on RBF and SOFM neural networks. Two stages are involved. First, get the freeway traffic flow model based on the RBF neural networks and use the model to obtain the output prediction. The residuals will be gotten from the comparison between the actual and prediction. Second, use a SOFM neural networks to classify the residuals to detect the incident. Because the SOFM has the character of topological ordering, the winning neuron's running trajectory on SOFM neuron array corresponds to the actual traffic state on freeway. We can observe the trajectory to detect the incident and achieve the visual traffic incident detection.