Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network

Wang Qiquan; Fei Yuzhou; Ni Junwen

doi:doi:10.11648/j.ajesa.20160401.11

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Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network

Wang Qiquan, Fei Yuzhou, Ni Junwen

Published in American Journal of Embedded Systems and Applications (Volume 4, Issue 1)

Received: 22 August 2016 Accepted: 9 September 2016 Published: 9 October 2016

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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.

Published in	American Journal of Embedded Systems and Applications (Volume 4, Issue 1)
DOI	10.11648/j.ajesa.20160401.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Urban Road Intersection, Warning System, BP Neural Network

References

[1]	Road traffic accident statistical yearbook of the People's Republic of China (2012). The ministry of public security traffic management bureau. 2013.5.
[2]	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.
[3]	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.
[4]	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.
[5]	Road traffic safety management planning [M]. Yan Baojie, Zhang Shengrui. Beijing: China railway mountain edition du, 2008.06. 1-3.
[6]	The world health organization, the World Bank. World road traffic injuries report [R] (2004), 2004.
[7]	The road traffic safety and the back reflection technology [M]. Liujian Jun Beijing: people's traffic mountain edition du, 2009. 19-57.
[8]	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.
[9]	Introduction to the road traffic safety [M]. Zheng Anwen, Yuan Hongwei. Beijing: mechanical industry mountain edition. 2010, 3-40.
[10]	Based on the application of BP neural network [J], Wang Linlin, Anyang normal college physics and electrical engineering college, Henan, Anyang, 2014, 1.
[11]	Analysis and improving way of BP ANN in predicting time series data [J]. WANG Wei, ZHANG Yingtang, Computer Engineering and Design, 2007, 28(21).

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APA Style

Wang Qiquan, Fei Yuzhou, Ni Junwen. (2016). Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network. American Journal of Embedded Systems and Applications, 4(1), 1-6. https://doi.org/10.11648/j.ajesa.20160401.11

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ACS Style

Wang Qiquan; Fei Yuzhou; Ni Junwen. Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network. Am. J. Embed. Syst. Appl. 2016, 4(1), 1-6. doi: 10.11648/j.ajesa.20160401.11

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AMA Style

Wang Qiquan, Fei Yuzhou, Ni Junwen. Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network. Am J Embed Syst Appl. 2016;4(1):1-6. doi: 10.11648/j.ajesa.20160401.11

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@article{10.11648/j.ajesa.20160401.11,
  author = {Wang Qiquan and Fei Yuzhou and Ni Junwen},
  title = {Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {4},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ajesa.20160401.11},
  url = {https://doi.org/10.11648/j.ajesa.20160401.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20160401.11},
  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.},
 year = {2016}
}

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TY  - JOUR
T1  - Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network
AU  - Wang Qiquan
AU  - Fei Yuzhou
AU  - Ni Junwen
Y1  - 2016/10/09
PY  - 2016
N1  - https://doi.org/10.11648/j.ajesa.20160401.11
DO  - 10.11648/j.ajesa.20160401.11
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20160401.11
AB  - 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.
VL  - 4
IS  - 1
ER  -

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Author Information

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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Plain Text BibTeX RIS

APA Style

Wang Qiquan, Fei Yuzhou, Ni Junwen. (2016). Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network. American Journal of Embedded Systems and Applications, 4(1), 1-6. https://doi.org/10.11648/j.ajesa.20160401.11

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ACS Style

Wang Qiquan; Fei Yuzhou; Ni Junwen. Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network. Am. J. Embed. Syst. Appl. 2016, 4(1), 1-6. doi: 10.11648/j.ajesa.20160401.11

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AMA Style

Wang Qiquan, Fei Yuzhou, Ni Junwen. Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network. Am J Embed Syst Appl. 2016;4(1):1-6. doi: 10.11648/j.ajesa.20160401.11

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@article{10.11648/j.ajesa.20160401.11,
  author = {Wang Qiquan and Fei Yuzhou and Ni Junwen},
  title = {Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {4},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ajesa.20160401.11},
  url = {https://doi.org/10.11648/j.ajesa.20160401.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20160401.11},
  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.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network
AU  - Wang Qiquan
AU  - Fei Yuzhou
AU  - Ni Junwen
Y1  - 2016/10/09
PY  - 2016
N1  - https://doi.org/10.11648/j.ajesa.20160401.11
DO  - 10.11648/j.ajesa.20160401.11
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20160401.11
AB  - 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.
VL  - 4
IS  - 1
ER  -

Copy | Download