基于网格化的路表温度感知技术

doi:10.13229/j.cnki.jdxbgxb.20221353

摘要/Abstract

摘要：

为实现路表温度的高精度预测，考虑路域环境的周期性和道路结构在纵向空间上的确定性特征，提出了包含天气模式的路表温度网格化感知方法，并基于试验路段的大量实测数据进行数据挖掘和分析。依据层次聚类算法的两种不同距离函数建立了两种感知网络模型（欧氏距离模型和相关距离模型）。利用平均绝对误差、均方根误差和平均相对误差对模型的预测效果进行评价。通过对3类天气模式下模型的预测误差水平进行分析，对预测值与实测值进行对比。结果显示：本文提出的网格化感知方法对天气模式较为敏感，两种模型均在抑制天气下的预测效果最好，抑制和中性天气下平均相对误差小于5%，极端天气下最大平均相对误差为5.43%。模型均方根误差在极端天气下最大为1.2 ℃，其他天气模式下均小于1 ℃。

关键词: 道路工程, 路表温度, 网格化感知, 聚类算法

Abstract:

In order to realize high-precision prediction of road surface temperature， considering the influence of the road environment and the characteristics of the road structure in the longitudinal direction， a gridding monitoring method for dividing the asphalt pavement road surface temperature of the weather type was proposed， and conducted data mining and analysed based on a large amount of measured data of the test road section. Two perceptual network models （Euclidean distance model and correlation distance model） were established according to the logic of two different distance functions of the hierarchical clustering algorithm， the prediction accuracy of the model was validated by the measured data， and the evaluation and comparison of performance of the models were carried out by the mean absolute error （MAE）， root mean square error （RMSE） and mean absolute percentage error （MAPE）， based on which the applicability of the two models was discussed. By comparing estimated value and measured value， the results show that the gridding monitoring method proposed is sensitive to weather condition， the MAPE of the models is less than 5% on cloudy and overcast weather， and the maximum MAPE is 5.43% on clear weather. The RMSE of the model reach maximum value of 1.2 ℃ in clear weather and is less than 1 ℃ in other weather condition.

Key words: road engineering, pavement surface temperature, sensing gridding, clustering algorithm

中图分类号:

U416.2

刘状壮,郑文清,郑健,李轶峥,季鹏宇,沙爱民. 基于网格化的路表温度感知技术[J]. 吉林大学学报(工学版), 2023, 53(6): 1746-1755.

Zhuang-zhuang LIU,Wen-qing ZHENG,Jian ZHENG,Yi-zheng LI,Peng-yu JI,Ai-min SHA. Pavement surface temperature monitoring method based on gridding approach[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(6): 1746-1755.

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天气模式	单元1	单元2	单元3	单元4	单元5	单元6	单元7	单元8	单元9	单元10
中性	1.171	0.584	0.583	1.072	0.479	0.789	0.869	0.707	0.915	1.118
极端	0.624	0.380	0.395	0.956	0.255	0.294	0.421	0.382	0.359	1.010
抑制	2.148	1.381	1.500	1.975	1.368	1.731	1.604	1.468	1.684	2.151

方法	相关系数	均方差/标准差	均方根误差	残差均值	平均绝对误差	平均绝对百分比误差
谈至明^［12］	-	0.85~0.99	-	0.36~0.24	-	-
庄传仪^［13］	0.897~0.982	2.443	-	-	-	-
杨书杰^［17］	-	-	1.18~1.86	-	-	-
林子静^［22］	0.96	-	1.7	0.09	-	-
王可心^［23］	-	-	1.09	-	0.68	-
本文	0.937~0.986	-	0.623~0.75	-	0.481	3.53