Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (6): 1746-1755.doi: 10.13229/j.cnki.jdxbgxb.20221353

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Pavement surface temperature monitoring method based on gridding approach

Zhuang-zhuang LIU1,2(),Wen-qing ZHENG1,Jian ZHENG1,3,Yi-zheng LI1,Peng-yu JI1,Ai-min SHA1,2   

  1. 1.School of Highway，Chang'an University，Xi'an 710064，China
    2.Key Laboratory of Highway Engineering in Special Region，Ministry of Education，Chang'an University，Xi'an 710064，China
    3.Shenzhen Branch，Wuhan Iron & Steel Design & Research Institute，Shenzhen 518028，China
  • Received:2022-10-23 Online:2023-06-01 Published:2023-07-23

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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

CLC Number: 

  • U416.2

Fig.1

Schematic diagram of the road domain environment grid"

Fig.2

Implementation path of the meshed perception model"

Fig.3

Schematic diagram of clustering threshold search principle"

Fig.4

Schematic diagram of truncation threshold selection"

Fig.5

Road surface temperature observation segments and meshing"

Table 1

Variance of each unit"

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1.1710.5840.5831.0720.4790.7890.8690.7070.9151.118
极端0.6240.3800.3950.9560.2550.2940.4210.3820.3591.010
抑制2.1481.3811.5001.9751.3681.7311.6041.4681.6842.151

Fig.6

Clustering of cloudy weather"

Fig.7

Clustering of clear weather"

Fig.8

Clustering of overcast weather"

Fig.9

Deviation diagram of Euclidean distance model"

Fig.10

Euclidean distance model error"

Fig.11

Deviation diagram of correlation coefficient model"

Fig.12

Correlation coefficient model error"

Fig.13

Errors comparison of each model under different weather conditions"

Table 2

Comparison of road surface temperature prediction errors in different papers"

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］0.897~0.9822.443----
杨书杰［17］--1.18~1.86---
林子静［22］0.96-1.70.09--
王可心［23］--1.09-0.68-
本文0.937~0.986-0.623~0.75-0.4813.53
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