基于编码器-解码器结构的路面平整度预测

Pavement Roughness Prediction Based on Encoder-decoder Structure

摘要:

提出了基于编码器?解码器结构的路面平整度预测模型。对比分析了不同网络层的表现，并比较了网络层个数、隐藏节点数、数据时间窗口对模型精度的影响。在美国交通部公开的LTPP（long-term pavement performance）数据库的基础上构建了国际平整度指数（IRI）数据集，并对模型进行了训练和评估。结果表明：采用门控循环单元（GRU）网络层的编码器?解码器结构的预测性能最好，优于经典的机器学习模型XGBoost和单独长短期记忆（LSTM）网络。通过特征随机打乱的方式对不同输入特征的重要性进行了评估，结果显示路面结构和温度对于路面平整度预测比较重要，在数据库建设时应注意对这些数据的收集。

Abstract:

A pavement roughness prediction model based on encoder-decoder structure was proposed， and a comparative analysis of different layers was conducted. Then， the effect of the layer number， the number of hidden units and the time window length on the accuracy of the model was discussed. To train and evaluate the model， an international roughness index （IRI） dataset was constructed based on long-term pavement performance （LTPP） database published by the US Department of Transportation. The results show that the encoder-decoder structure with gated recurrent unit （GRU） layer has the highest accuracy， its performance is better than that of the machine learning model XGBoost and single long short-term memory （LSTM） network. The importance of different input features was evaluated by randomly shuffling features， and the results indicate that the road structure and temperature are important for pavement roughness prediction. Therefore， the road structure and temperature data should be attached great importance during the construction of pavement database.