The ability to forecast road surface temperature (RST) in advance is an important asset for winter maintenance (WRM) operations. It effectively allows for the reduction of WRM cost through more efficient use of their maintenance resources. However, considering extensive road networks that must be monitored and the extent at which RST varies over time, WRM agencies have constantly been looking for better ways to generate accurate road weather forecasts as they strive to optimize their WRM services and maintain safe travels. To tackle this, this study developed RST forecasting models using an Artificial Neural Network (ANN). RST measurements collected by six selected stationary road weather information systems (RWIS) stations in Alberta, Canada were utilized to validate the feasibility and applicability of the proposed method developed herein. The developed models were found to generate highly accurate results with mean absolute error (MAE) values of 0.64, 1.20, 1.59, 2.16, 2.56, and 3.03 °C for 1-h, 2-h, 3-h, 4-h, 5-h, and 6-h ahead forecasts, respectively. The novelty of this study lies in investigating the probable effect of some external factors on model performance, where it was revealed that forecasting horizon and geographical attributes influenced forecasting accuracies. Upon investigating the hypothesis that locational attributes would affect forecasting accuracies, the results confirmed that accuracy improved with increasing latitude, and decreasing elevations – worthwhile findings that can potentially lead to developing more refined models for generating highly accurate location-specific RST forecasts.

提前预测路面温度 (RST) 的能力是冬季维护 (WRM) 运营的重要资产。它通过更有效地使用维护资源有效地降低了 WRM 成本。然而，考虑到必须监测的广泛的道路网络以及 RST 随时间变化的程度，WRM 机构一直在寻找更好的方法来生成准确的道路天气预报，因为他们努力优化其 WRM 服务并保持安全旅行。为了解决这个问题，本研究使用人工神经网络 (ANN) 开发了 RST 预测模型。加拿大艾伯塔省六个选定的固定道路气象信息系统 (RWIS) 站收集的 RST 测量值用于验证本文开发的提议方法的可行性和适用性。开发的模型在 1 小时、2 小时、3 小时、4 小时的平均绝对误差 (MAE) 值为 0.64、1.20、1.59、2.16、2.56 和 3.03 °C 时产生高度准确的结果，分别提前 5 小时和 6 小时预测。本研究的新颖之处在于调查了一些外部因素对模型性能的可能影响，其中揭示了预测范围和地理属性会影响预测准确性。在调查位置属性会影响预测准确性的假设后，结果证实准确性随着纬度的增加和海拔的降低而提高 - 有价值的发现可能会导致开发更精细的模型以生成高度准确的特定位置的 RST 预测。