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Computational Intelligence and Neuroscience
Volume 2014, Article ID 892132, 11 pages
Research Article

A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

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

Received 14 July 2014; Accepted 5 October 2014; Published 4 November 2014

Academic Editor: Xiaobei Jiang

Copyright © 2014 Pengfei Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.