环胶州湾高速路面低温气候特征和SVM预报模型

Climatic characteristics and SVM forecast model of subfreezing road temperature on the expressways around Jiaozhou Bay

利用环胶州湾(青兰高速起始段及胶州湾大桥)14个高速交通气象观测站逐5 min监测资料,统计分析了冬季环胶州湾高速路面低温(路面温度t<0℃)出现频率、出现时间特征及其与气象要素的相关性。结果表明:冬季环胶州湾高速路面结冰风险普遍较高,空间分布上呈现“西南高东北低”的特点;逐时最低路面温度日变化趋势与逐时最低气温基本一致,全天各时次最低路面温度均高于最低气温;14:00气温、路面温度和18:00相对湿度与次日最低路面温度呈正相关,18:00风速与次日最低路面温度呈负相关。经过参数调优的支持向量机(support vector machine,SVM)模型预报路面温度t≤0℃准确率达到87.78%,高于多元线性回归模型,并在独立性检验中得到了验证,对实际预报服务具有指导意义。

The data used in this study is the 5-min observations of 14 traffic weather monitoring stations located at the beginning section of G22 Qinglan Expressway and Jiaozhou Bay Bridge.The frequency and occurrence time of subfreezing temperatures(t<0℃)in winter on the expressways around Jiaozhou Bay and their correlations with meteorological variables are statistically analyzed.The results are as follows.The risk of icy roads in winter is generally high in the study area,where the frequency of subfreezing road temperature is higher in the southwest than in the northeast.The diurnal variation of the hourly minimum road temperature follows the same pattern with the hourly minimum air temperature,and throughout the day,the minimum road temperature each hour is higher than the minimum air temperature.The air temperature and road temperature at 14:00,relative humidity at 18:00 are positively correlated with the next-day minimum road temperature,while the wind speed at 18:00 is negatively correlated with it.The prediction accuracy of t≤0℃by the support vector machine(SVM)model with optimized parameters is 87.78%,higher than that by the multiple linear regression model.The improved performance is verified in the test of independence,which offers significant guidance for the application in icy road forecast service.