摘要
为进一步提高雾天交通安全气象保障精细化能力,以江苏、安徽高速公路雾事故多发路段为例,利用2012—2018年事故信息与气象资料,建立一种基于变量选择和特征提取的逐时雾事故判别支持向量机模型。模型参照递归特征消除思路选择事故发生时间、地理位置、气象环境等重要变量,使用主成分分析提取重要变量的主要特征,并以径向基为核函数、以网络搜索确定最优参数。结果表明:结合重要变量选择和主成分分析的支持向量机混合模型能够成功识别出训练集81.4%和测试集83.0%的事故样本,AUC分数均为0.946;判别效果优于支持向量机单独算法,以及仅基于重要变量选择或主成分分析的支持向量机算法;3个典型实例分析也说明该模型对于阶段性或持续性大雾天气下的交通事故发生有一定判识与警示意义。
In order to further improve the ability of refined meteorological services for traffic safety in foggy weather,this study takes Jiangsu and Anhui expressway sections where frequent fog-caused accidents happen as examples,with the application of the disaster information and weather data from 2012 to 2018 to establish a support vector machine hybrid model for hourly fog accident detection based on variable selection and feature extraction.The model uses the recursive feature elimination method to select the important variables from accident time,geographic location,and meteorological environment,and then extracts the main features of the important variables by principal component analysis.The radial basis is used as the kernel function,and the optimal parameters are determined by network search.The results show that this support vector machine hybrid model can successfully identify 81.4%of the accident samples in the training set and 83.0%of the test set,and the AUC scores are both 0.946.The ability to identify fog accidents is superior to the support vector machine algorithm and the support vector machine algorithm based only on main variable selection or principal component analysis.The analysis of three typical examples also shows that the support vector machine hybrid model has certain identification and warning significance for the occurrence of traffic accidents under periodic or persistent foggy weather.
作者
宋建洋
田华
郜婧婧
王志
李蔼恂
陈运
SONG Jianyang;TIAN Hua;GAO Jingjing;WANG Zhi;LI Aixun;CHEN Yun(Public Meteorological Service Center,China Meteorological Administration,Beijing 100081;Key Laboratory of Transportation Meteorology,China Meteorological Administration,Nanjing 210009)
出处
《气象科技》
2023年第1期149-156,共8页
Meteorological Science and Technology
基金
国家重点研发计划项目(2020YFB1600100、2018YFC1505503)
中国气象局公共气象服务中心创新基金项目(K2021002)资助。
关键词
高速公路
雾天交通事故判别
逐小时概率
变量选择
主成分分析
支持向量机
expressway
fog traffic accident detection
hourly probability
variable selection
principal component analysis
support vector machine
