北方公路交通气象环境识别及安全管理策略研究

用沈山高速公路沿线10个交通气象站的逐10 min气温、能见度、路面温度、降水、湿度和风等资料,采用多元逐步线性回归、最小二乘曲线拟合、MLP神经网络建立数学模型对北方公路路面温度、能见度以及冰雪路面等交通气象环境进行识别。通过抽取随机样本对模型进行检验,结果表明:所建立的模型对交通气象环境具有较好的模拟和识别作用。基于交通工程理论建立的不同路况条件下车速预估和交通安全指数模型对公路交通安全管理具有很好的应用价值。

The meteorological environment identification and security management policy study of road traffic in northern China

Weather variables and statistic methods are used to built up land surface temperature and visibility forecasting function and ice-snow recognition model, in which variables including air temperature, visibility, land surface temperature, precipitation, relative humidity and wind are collected at 10 meteorological observing stations along Shen-Shan highway at 10 minutes interval, and statistic methods include stepwise regression, least square technique and MLP neural network. Test results by random sampling indicate that the models can simulate and identify traffic meteorological environment.Finally, land surface condition for different kind of roads based on traffic engineering theory, vehicle speed predictor under different visibility conditions and traffic security index are effective and reliable for traffic security management.