兵工自动化Issue(4)：68-70,3.DOI:10.7690/bgzdh.2014.04.018
基于概率神经网络的高速公路交通事故多发点安全预警模型
An Early-Warning Model of Freeway Hazardous Locations Based on Probabilistic Neural Network
周志宏 1李学波1
作者信息
- 1. 蚌埠汽车士官学校司训勤务系，安徽 蚌埠 233011
- 折叠
摘要
Abstract
Traffic accidents on freeway hazardous locations are hard to predict, to solving this problem, an early-warning model was made by using the nonlinear approximation capability with the pattern classification function of the probabilistic neural network (PNN). By designed the probabilistic neural network topology structure, provided traffic state categories, determined the index system of related traffic accidents, sketched out the learning process of the probabilistic neural network, the properties were also tested via the Matlab simulation experiment. Results indicate that, the early-warning model with the PNN recognition technology achieves quite high detection accuracy, and the ability of generalization is well, can be used at freeway traffic safety real-time monitoring, and as an effective prevention and control approach against the factors causing road traffic hazards is entirely possible.关键词
概率神经网络/安全预警/模式分类Key words
probabilistic neural network/early-warning/pattern classification分类
信息技术与安全科学引用本文复制引用
周志宏,李学波..基于概率神经网络的高速公路交通事故多发点安全预警模型[J].兵工自动化,2014,(4):68-70,3.
