关键词: 路面温度, 道路类型, 温度异质性, 预报模型

Abstract:

To investigate the variation patterns of asphalt pavement temperature in complex urban road environments, this study conducted wintertime (from November 2024 to February 2025) observations on six representative road sections across three typical road types in the urban area of Tianjin: overpasses, arterial roads, and cross-river bridges. The heterogeneous characteristics of asphalt pavement temperature and the underlying formation mechanisms for these three road types were analyzed. Based on enhanced feature variables, Lasso regression, polynomial regression, and support vector regression models were established for asphalt pavement temperature prediction. The findings reveal significant differences in the asphalt pavement temperature characteristics of the three road types, attributable to variations in their thermal properties of underlying surfaces and local energy exchange processes. Cross-river bridges exhibit the largest diurnal range, the highest rate of temperature change, and the most extreme temperatures due to their low thermal mass structures and efficient heat exchange between the bridge and air. Overpasses, characterized by massive structures with high thermal inertia, show the most gradual temperature changes. Arterial roads, regulated by heat storage in the soil subgrade and anthropogenic heat sources, display moderate diurnal variation and temperature change rates, with higher nighttime minimum temperatures than the bridges. In terms of modeling, integrating periodic and time-lag features effectively improves model prediction accuracy compared to using only original meteorological features. For the Lasso model, the RMSE is reduced by approximately 18% on average, ranging from about 1.04 to 1.61 ℃, with MAE ranging from about 0.74 to 1.33 ℃. The Lasso, polynomial, and support vector machine models all capture the variation patterns of asphalt pavement temperature well. Among them, the Lasso model maintains high prediction accuracy while offering greater interpretability and computational efficiency, thus presenting higher application value. Error analysis shows that the prediction error of the Lasso model is higher during the daytime than at night, indicating its limited ability to characterize high-frequency abrupt processes strongly influenced by shortwave radiation. Prediction errors for temperature extremes on cross-river bridges are relatively larger, exhibiting an obvious "peak suppression and trough filling" phenomenon. This is related to the structural thermal characteristics and the model training mechanism aimed at minimizing overall error. This study clarifies the heterogeneity of asphalt pavement temperatures in complex urban roads and its physical causes, providing a scientific basis for differentiated road-icing warnings and precise management.

Key words: asphalt pavement temperature, roads types, temperature heterogeneity, prediction models