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Dynamic traffic safety grade evaluation model for road sections based on gray fixed weight clustering

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成果类型:
期刊论文
作者:
Jing, H.L.;Ye, L.T.;Wang, J.Z.;Xie, Z.;Brown, M.
通讯作者:
Xie, Z.(xiezheng@1128.com)
作者机构:
[Jing, H.L.] Department of Software Engineering, Xiamen Institute of Software Technology, Xiamen
361024, China
School of Computer Science, China University of Geosciences, Wuhan
430074, China
[Ye, L.T.] Department of Art Design, Xiamen Institute of Software Technology, Xiamen
通讯机构:
[Xie, Z.] C
College of Management, Hunan City University, Yiyang, Hunan, China
语种:
英文
关键词:
BP neural network;Clustering weight;Complex and scattered;Gray fixed weighted clustering;Road section;Traffic safety grade
期刊:
Advances in Transportation Studies
ISSN:
1824-5463
年:
2018
卷:
2
期:
Special issue
页码:
15-24
基金类别:
This work was supported by the cooperative innovation project of Xiamen science and Techno logy Bureau no.3502Z20163017, and the cooperative innovation project of Xiamen science and T echnology Bureau no.3502Z20163018.
机构署名:
本校为通讯机构
院系归属:
管理学院
摘要:
The conventional gray predication model GM (1, 1) cannot accurately analyze the dynamic traffic index information of complex and scattered road sections because it may cause relatively large error and performs not well in stability. In order to solve this problem, a dynamic traffic safety grade evaluation model for road sections based on gray fixed weight clustering is designed. In this method, In this method, the gray clustering evaluation method is adopted for gray clustering to complex and scattered traffic safety grade evaluation indexes, a...

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