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Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network
American Journal of Embedded Systems and Applications
Volume 4, Issue 1, November 2016, Pages: 1-6
Received: Aug. 22, 2016; Accepted: Sep. 9, 2016; Published: Oct. 9, 2016
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Authors
Wang Qiquan, Safety Engineering, China Institute of Industrial Relations, Beijing, China
Fei Yuzhou, Safety Engineering, China Institute of Industrial Relations, Beijing, China
Ni Junwen, Safety Engineering, China Institute of Industrial Relations, Beijing, China
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Abstract
The urbanization is the sign of advanced development for an urban. In recent years, with the development of science, technology and economy and the rise of urban car ownership, urban road traffic became a severe problem. There occurred a huge number of urban road traffic accidents frequently. To study and find insufficiency for the research status at home and abroad, the four aspects --"man - vehicle - road - environment" are analyzed, and the comprehensive analysis of the present safety situation of urban road intersection is made. Selecting one in seven important influencing factors of urban road intersection index as a Back Propagation (BP) neural network input, the early warning model, based on BP neural network, is established. Data of existing urban road intersections is analyzed, and the results show that the BP neural network can be well applied to early warning and forecast model analysis of urban road intersection accident, thus it facilitates for the traffic administrative department of the city road intersection to predict the accident frequency of urban road intersection for the traffic accident in the future, take appropriate intervention measures and improve the safety status of urban road intersection.
Keywords
Urban Road Intersection, Warning System, BP Neural Network
To cite this article
Wang Qiquan, Fei Yuzhou, Ni Junwen, Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network, American Journal of Embedded Systems and Applications. Vol. 4, No. 1, 2016, pp. 1-6. doi: 10.11648/j.ajesa.20160401.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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