New method for identifying road traffic accident-prone locations based on BP neural network

被引：0

作者：

Harbin Institute of Technology, Harbin 150001, China ^{[1
]}

机构：

来源：

Tumu Gongcheng Xuebao | 2008年 / 6卷 / 108-111期

关键词：

Backpropagation - Neural networks - Highway accidents - Roads and streets - Traffic control - Accident prevention - Motor transportation;

D O I：

暂无

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学科分类号：

摘要：

Safety of road has huge influences on human health and economy of the society, and draws more and more attention all over the world. Traffic accident-prone location plays an important part in studying the relationship between road and accident, an important way for road safety improvement. Identification and improvement of accident-prone locations is an effective means to improving the condition of road safety, and the most economic at most times. This paper puts forward an application of the artificial neural network BP arithmetic to identify road traffic accident-prone locations based on commonly used identification methods, which provided a new way of thinking on how to determine road accident-prone locations and enriched the basic theory of traffic accident-prone locations identification. Base on influencing factors in the road traffic safe system through integration analysis, an appraisement index is provided for traffic accident-prone locations identification.

引用

共 50 条

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