为更好地开展公路交通道路结冰预报预警服务工作,利用甘肃省道路结冰高发区路段(甘肃武威以东)的交通气象站逐小时观测资料,分析道路结冰空间分布特征,探讨道路结冰与气象要素的相关性,采用Logistic回归法和神经网络算法构建道路结冰预警模型.结果表明:甘肃省道路结冰主要集中在冬季(12月至次年2月),其中00:00-10:00和22:00-23:00(北京时)出现道路结冰的频率较高.Logistic回归模型和神经网络模型对未发生结冰事件的预测准确率较高,分别为91.9%和96.2%;针对发生结冰事件,Logistic回归模型的预测准确率较低,为31.6%,而神经网络模型的预测准确率可达44.6%,说明2种模型对道路结冰预警有一定指示意义,神经网络模型预测效果优于Logistic回归模型.
In order to better carry out the road icing prediction and early warning services,the hourly observation data of traffic meteo-rological stations in the high incidence area of road icing in Gansu Province(the east of Wuwei,Gansu)were used to analyze the spatial and temporal distribution characteristics of road icing,explore the correlation between road icing and meteorological factors,and con-struct the road icing warning model by using Logistic regression method and neural network algorithm.The results showed that road ic-ing in Gansu Province occurred mainly in winter(from December to February of the following year),and the frequency of road icing was higher from 00:00 to 10:00 and from 22:00 to 23:00.Logistic regression model and neural network model had high prediction accuracy for non-icing events,with 91.9%and 96.2%,respectively.For the occurrence of icing events,the prediction accuracy of Logistic regres-sion model was low,at 31.6%,while that of neural network model could reach 44.6%,indicating that the two models had certain indica-tive significance for road icing warning,and the prediction effect of neural network model was better than that of Logistic regression model.