用图神经网络补全道路状况数据缺失，提升养护决策准确性。
Pavement Missing Condition Data Imputation through Collective Learning-Based Graph Neural Networks
- 基于邻近路段特征与条件依赖关系建模
- 在德克萨斯州数据上实现高精度缺失值填补
- 适合交通基础设施智能运维研究者使用
道路状况数据对评估路网状态及制定养护修复需求至关重要。但由于传感器故障和非周期性巡检等原因，数据常出现缺失，尤其是系统性缺失会带来信息损失、统计效能下降和评估偏差。现有方法多通过剔除含缺失值的数据点或基于相关性进行填补。本文提出一种基于集体学习的图卷积网络，融合相邻路段特征与观测路段间的条件依赖关系，以学习缺失的路面状况值。该方法能有效捕捉相邻路段状况之间的依赖关系。案例研究采用德克萨斯州交通部奥斯汀分部采集的道路数据，实验表明该模型在缺失数据填补方面表现优异。
原文摘要 · Abstract (English)
Pavement condition data is important in providing information regarding the current state of the road network and in determining the needs of maintenance and rehabilitation treatments. However, the condition data is often incomplete due to various reasons such as sensor errors and non-periodic inspection schedules. Missing data, especially data missing systematically, presents loss of information, reduces statistical power, and introduces biased assessment. Existing methods in dealing with missing data usually discard entire data points with missing values or impute through data correlation. In this paper, we used a collective learning-based Graph Convolutional Networks, which integrates both features of adjacent sections and dependencies between observed section conditions to learn missing condition values. Unlike other variants of graph neural networks, the proposed approach is able to capture dependent relationship between the conditions of adjacent pavement sections. In the case study, pavement condition data collected from Texas Department of Transportation Austin District were used. Experiments show that the proposed model was able to produce promising results in imputing the missing data.
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