| For a long time,the traffic safety situation in China has been severely affected by meteorological conditions with low visibility,especially low-visibility meteorology which is represented by foggy weather.Many problems were caused by low-visibility meteorology,such as environmental pollution,traffic accidents,traffic delays,and breakage to basic facilities.Low-visibility weather not only increases the cost of travel,it also has a serious impact on the public travel and makes serious losses to social and economic.In this paper,the foggy weather which can affect visibility is studied,and the characteristics of fog change and meteorological conditions are analyzed with the example of Henan to Guangdong highway in Beijing-Zhuhai expressway.Based on artificial neural network algorithm,the foggy weather forecast model of low visibility is built,and the forecast model is tested by the historical sample data of thirty-three sites along the highway in eight years.The foggy weather forecast system is constructed by the model.The indicators’ selection and evaluation method of traffic condition evaluation in foggy weather are analyzed and studied.The comprehensive index of early warning and the grade of early warning are formulated.Finally,the technical measures for traffic safety control are given.This article provides a reference for the construction of the observation station and observation network in foggy weather in China,and at the same time,it improves the pertinence and practicability of forecast and early warning in foggy weather,and promotes popularization and application of mature technology experience in traffic safety management under conditions which are in low visibility and foggy weather.At last,it helps to promote the level of traffic safety control under the condition of foggy weather,the standard construction of low visibility fog information system,and improve the capability of service and decision support for integrated traffic meteorological information. |
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