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Comput Intell Neurosci. 2014;2014:892132. doi: 10.1155/2014/892132. Epub 2014 Nov 4.

A red-light running prevention system based on artificial neural network and vehicle trajectory data.

Author information

1
Transportation School, Southeast University, 2 Sipailou, Nanjing 210096, China ; School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA.
2
Highway School, Chang'an University, The Middle Section of Southern Second Ring Road, Xi'an 710064, China.
3
Transportation School, Southeast University, 2 Sipailou, Nanjing 210096, China.

Abstract

The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

PMID:
25435870
PMCID:
PMC4236967
DOI:
10.1155/2014/892132
[Indexed for MEDLINE]
Free PMC Article

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