Application of Three Statistical Forecast Models in Early Warning of LowTemperature on Road Surface in Jiangsu and Their Comparison
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Abstract:
In order to provide a better service for the warning of low temperature disasters on road surface and mitigate the damage caused by the frozen road against cars, in this paper, the observed data of traffic meteorological factors in the Automatic Weather Monitoring System (AWMS) on the Jiangsu expressway network from 2012 to 2016 are collected to analyze the temporal and spatial pattern of low temperature occurrence on the road surface and the statistic model establishment and the forecast experiments of the low temperature warning on road surface are carried out through three statistical forecast methods: the Multiple Linear Regression, the Naive Bayes Method, and the Support Vector Machine Model. The results are showed as follows: (1) 〖JP2〗The occurrence frequency of low temperatures below 0 ℃, below -2 ℃〖JP〗 and below -5 ℃ on the road surface of the expressway network in Jiangsu Province displayed the distributions of “higher in the north part and lower in the south part.” (2) The road surface temperature below 0 ℃ on the expressway network occurs between 15:00 and 06:00 of the next day in general. (3) In the model establishment and forecast experiments of a single station for M9308 Station on the Jiangsu section of the BeijingShanghai Expressway, it is found that the forecast models taking the air temperature at 13:00, the variation of air temperature from 13:00 to 18:00, the road temperature at 13:00, the variation of road temperature from 13:00 to 18:00, the roadbed temperature at 13:00, the variation of roadbed temperature from 13:00 to 18:00, the relative humidity at 18:00, and the 〖WTBX〗U〖WTBZ〗 component of wind speed at 18:00 as the forecast factors have the best efficiency in the warning of road low temperature. The naive Bayes method has the highest forecasting accuracy rate in the three methods. (4) For the whole expressway network in Jiangsu, the accuracy rates of three statistical forecast models in the warning of low temperature on road surface are higher than 75%. The comparison of the low temperature forecast experiment results of road surface on the Jiangsu expressway network indicates that the Multiple Linear Regression shows the best warning efficiency in the northern Jiangsu with an accuracy rate larger than 85% and the Support Vector Machine Model displays the best warning efficiency in the southern Jiangsu with an accuracy rate higher than 95%.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.