An accurate road surface friction forecasting algorithm can allow travelers and managers to schedule trips and maintenance activities based on the road weather condition to enhance traffic safety and efficiency in advance. Previously, scholars developed multiple forecasting models to predict road surface conditions using historical data. However, historical dataset used for model training may have missing values caused by multiple issues, e.g. the data collected by on-vehicle sensors may be influenced when vehicles cannot travel due to high economic and labor cost or weather-related issues. The missing values in the road surface condition dataset can damage the effectiveness and accuracy of the existing prediction methods. This study proposed a road surface friction forecasting algorithm by employing a time-aware Gated Recurrent Unit (GRU-D) networks that integrate a decay mechanism as extra gates of the GRU to handle the missing values and forecast the road surface friction in future periods simultaneously. The evaluation results present that the proposed GRU-D networks outperform all selected baseline algorithms. The impacts of missing rate on predictive accuracy, learning efficiency, and learned decay rates are investigated as well. The findings can help improve the forecasting accuracy and efficiency of road surface friction prediction using historical data with missing values, therefore mitigating the negative impact of wet or icy road conditions on traffic safety and efficiency.

中文翻译：

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时间感知的门控循环单元网络，使用缺少值的历史数据预测路面摩擦

准确的路面摩擦预测算法可以使旅行者和管理人员根据道路天气状况安排旅行和维护活动，从而提前提高交通安全性和效率。以前，学者们开发了多种预测模型来使用历史数据预测路面状况。但是，用于模型训练的历史数据集可能由于多个问题而导致缺少值，例如，当车辆由于高昂的经济和人工成本或与天气相关的问题而无法行驶时，车载传感器收集的数据可能会受到影响。路面状况数据集中的缺失值可能会破坏现有预测方法的有效性和准确性。这项研究提出了一种采用时间感知的门控循环单元（GRU-D）网络的路面摩擦预测算法，该网络集成了一个衰减机制作为GRU的额外门来处理缺失值并同时预测未来的路面摩擦。评估结果表明，提出的GRU-D网络优于所有选定的基线算法。还研究了缺失率对预测准确性，学习效率和学习衰减率的影响。这些发现可以帮助使用具有缺失值的历史数据来提高路面摩擦力预测的预测准确性和效率，从而减轻潮湿或结冰的道路状况对交通安全和效率的负面影响。