冬季高速公路逐时路表温度LSTM预测模型  

Hourly road surface temperature LSTM prediction model of expressway in winter

作　　者：戴秉佑 杨文臣 戢晓峰[1] 周林义[4] 房锐 DAI Bingyou;YANG Wenchen;JI Xiaofeng;ZHOU Linyi;FANG Rui(School of Transportation Engineering,Kunming University of Science and Technology,Kunming Yunnan 650500,China;National Engineering Laboratory For Surface Transportation Weather Impacts Prevention,Broadvision Engineering Consultants Co.Ltd.,Kunming Yunnan 650200,China;Yunnan Key Laboratory of Digital Communications,Kunming Yunnan 650103,China;Key Laboratory of Transportation Meteorology,Nanjing Jiangsu 210008,China)

机构地区：[1]昆明理工大学交通工程学院,云南昆明650500 [2]云南省交通规划设计研究院有限公司陆地交通气象灾害防治技术国家工程实验室,云南昆明650200 [3]云南数字交通重点实验室,云南昆明650103 [4]中国气象局交通气象重点实验室,江苏南京210008

出　　处：《中国安全科学学报》2023年第1期136-144,共9页China Safety Science Journal

基　　金：国家重点研发计划项目(2022YFC3002601);交通运输行业重点科技项目(2018-MS4-102,ZL-2018-04);云南省科技厅基础研究计划项目(202101AT070693)。

摘　　要：为提高冬季路表温度的预测精度,提出一种基于多维长短时记忆(LSTM)神经网络的冬季路表温度逐时预测模型,以小时路表温度为模型输出,综合考虑多维气象因素的累积影响和路表温度的周期性,采用滑动窗口构造输入特征矩阵;构建路表温度LSTM逐时预测模型,通过深度学习高效逼近具有复杂非线性和不确定性的路表温度,并以江苏省宁宿徐高速公路、云南省麻昭高速公路为实例进行验证。结果表明:与随机森林(RF)模型和BP神经网络相比,LSTM路表温度逐时预测模型的准确率得到显著提高,在宁宿徐高速、麻昭高速的平均绝对误差(MAE)、均方误差(MSE)和均方根误差(RMSE)分别为0.303、0.295、0.543和0.581、0.694、0.833,预测值与观测值绝对误差位于[0,1)℃之间的占比为93%和89%。LSTM模型能准确捕捉路表温度的周期性和不确定性,在阴雨天和晴朗天的预测值与实测值基本一致,模型鲁棒性较好。To improve the prediction accuracy of winter road surface temperature,an hourly prediction model of road surface temperature of the expressway in winter based on multi-dimensional LSTM neural network was proposed.The hourly road surface temperature as the model output,the sliding window was used to construct a feature input matrix by considering the cumulative effect of multi-dimensional meteorological factors on pavement temperature and the periodicity of road surface temperature.The hourly road surface temperature prediction model based on LSTM was constructed to efficiently approximate the road surface temperature with complex nonlinearity and uncertainty by deep learning and was validated with examples of Ning-Su-Xu expressway in Jiangsu province and Ma-Zhao expressway in Yunnan province.The results show that compared with the random forest(RF)model and the BP neural network model,the hourly road surface temperature prediction accuracy of the proposed LSTM model is significantly improved,in which the mean absolute error(MAE),mean square error(MSE)and root mean square error(RMSE)of the LSTM model in Ning-Shu-Xu expressway and Ma-Zhao expressway are 0.303,0.295,0.543 and 0.581,0.694,0.833 respectively,and theabsolute error between the predicted value and the observed values is between[0,1)℃,accounting for 93%and 89%.The LSTM model accurately captures the periodicity and uncertainty of road surface temperature,with good model robustness,when the predicted values on rainy and sunny days are basically consistent with the measured values.

关 键 词：高速公路 路表温度 长短时记忆(LSTM)神经网络 逐时预测 气象因素 

分 类 号：X951[环境科学与工程—安全科学] U491[交通运输工程—交通运输规划与管理]