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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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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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.
Urban Road Intersection, Warning System, BP Neural Network
To cite this article
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.
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/
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Road traffic accident statistical yearbook of the People's Republic of China (2012). The ministry of public security traffic management bureau. 2013.5.
Analysis and research on road traffic safety evaluation [A]. Wang Qiquan, Feng Zhibin. China association of occupational safety and health. The Chinese association of occupational safety and health Years academic essays in 2007[C]. Occupational safety and health association: China, 2007-6.
The embankment settlement experiment research and numerical simulation [J]. Jian-min, Xiong Shengji, Yu Qintian. Journal of Huazhong University of science and technology (city science edition), 2008, 2008:54-56.
The establishment of the highway traffic safety early warning management system (English) [J]. Liu Qing, Wu Yanzi. Journal of Wuhan university of technology (transportation science and engineering), 01 2003:2003-424.
Road traffic safety management planning [M]. Yan Baojie, Zhang Shengrui. Beijing: China railway mountain edition du, 2008.06. 1-3.
The world health organization, the World Bank. World road traffic injuries report [R] (2004), 2004.
The road traffic safety and the back reflection technology [M]. Liujian Jun Beijing: people's traffic mountain edition du, 2009. 19-57.
Road traffic safety management in our country main problem analysis and countermeasures study [J]. Zhou Xin, Wanshou en.The safety production science and technology of China. 2007. 3.
Introduction to the road traffic safety [M]. Zheng Anwen, Yuan Hongwei. Beijing: mechanical industry mountain edition. 2010, 3-40.
Based on the application of BP neural network [J], Wang Linlin, Anyang normal college physics and electrical engineering college, Henan, Anyang, 2014, 1.
Analysis and improving way of BP ANN in predicting time series data [J]. WANG Wei, ZHANG Yingtang, Computer Engineering and Design, 2007, 28(21).