Wen Li
ABSTRACT
The extreme temperatures experienced by highway pavements significantly impact road traffic, primarily stemming from meteorological disasters such as unusual high air temperatures, snowstorms, and freezing conditions. However, existing approaches fail to adequately account for the influence of meteorological factors. Developing a rational and effective modeling approach is crucial for precise predictions of highway pavement extreme temperatures. Such predictions not only help prevent traffic accidents but also offer reliable decision-making support for road maintenance and traffic management. In this research, we present an efficient short-term prediction model for highway pavement temperatures, based on a multi-layered Gated Recurrent Unit (GRU) framework. This model comprehensively incorporates the impact of meteorological factors on pavement temperatures and is subsequently validated using meteorological data collected from a highway section managed by the Zhaotong District Operation Department of Yunnan Communications Investment & Construction Group CO., LTD. We conduct comparisons across various prediction time intervals and find that the model's optimal performance occurs at a 1.5-hour prediction step, with lowest Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) values. Moreover, the experimental outcomes indicate that our proposed model's performance outperforms that of other competing models, including BiGRU-attention, BiLSTM-attention, Muti-RNN, and Muti-LSTM. Our experimental results demonstrate that the method proposed in this study effectively predicts extreme highway pavement temperatures.
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Digital Library
- Adwan I, Milad A, Memon Z A, Asphalt Pavement Temperature Prediction Models: A Review. Applied Sciences, 11(9), 3794, 2021.Google ScholarCross Ref
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Index Terms
- Highway pavement temperature short-term prediction model based on multi-layer GRU
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