A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data
Joint Authors
Li, Yan
Guo, Xiucheng
Li, Pengfei
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-11-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Pengfei& Li, Yan& Guo, Xiucheng. 2014. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1016752
Modern Language Association (MLA)
Li, Pengfei…[et al.]. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1016752
American Medical Association (AMA)
Li, Pengfei& Li, Yan& Guo, Xiucheng. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1016752
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1016752