摘要
道路交通事故黑点的预测鉴别是改善道路交通安全状况最重要、最关键的一步,采用基于GA-BP神经网络算法与粗糙集理论相结合的方法建立交通事故黑点预测模型.分析天津市津围公路的交通事故统计数据,通过GA-BP神经网络算法建立静态单元,考虑静态道路状况,分析得出道路的事故黑点样本.考虑实时动态道路交通环境条件的影响,并利用粗糙集理论建立有效的交通道路事故黑点预测模型,2种理论的有机结合,减少糙杂繁冗的数据量,降低伪报警率,提高事故黑点的预报精度,并通过实例进行实证分析.
Prediction and identification of traffic accident black spot is the most important and critical step to improve road traffic safety.A new prediction model of traffic accident black spots is proposed based on GA-BP neural network algorithm and rough set theory.First,the traffic accident statistics of Jinwei Road in Tianjin are analyzed.With consideration of static road conditions,the samples of road accident black spots are obtained by the GA-BP neural network algorithm.Furthermore,an effective road traffic accident black spot prediction model is established by utilizing rough set theory with consideration of the impact of real time dynamic conditions.A numerical example is illustrated.Experimental results show the proposed model with the combination of these two theories can reduce the hybrid and burdensome amount of data,lower the false alarm rate and improve the forecasting accuracy of accident black spots.
出处
《武汉理工大学学报（交通科学与工程版）》
2011年第4期756-760,共5页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
基金
河北省科技厅科技攻关项目资助(批准号:062135138)
关键词
交通事故黑点
遗传算法
BP神经网络
粗糙集
属性重要度
traffic accident black spots
genetic algorithm
BP neural network
rough set
attribute importance
