With the advent of intelligent transportation systems (ITS), spatiotemporal traffic data has gained growing importance in real-time monitoring, prediction, and control of traffic. However, in practical implementations, data collection devices are often faced with malfunctions caused by various unpredictable disruptions, thereby resulting in the so-called “missing value problems.” In realistic cases, the disruptions to the data collection devices are often associated with some key events (e.g., power cut and natural disasters), in addition, along with other disruptions the missing value problem could be in a complicated manner with both randomly and completely missing patterns. To perform the imputation task with such complicated missing patterns, we propose a hybrid spatiotemporal method which utilizes the time series properties by “prophet” model and captures the spatial residuals information by iterative random forest model. The spatiotemporal method first applies the temporal part to fill the missing value and then adopts the spatial part to acquire the residual component of the missing values. The results of the two components are integrated into the final imputations. Based on the PeMS freeway dataset (PeMS, 2019) and an urban road dataset under extensive artificially designed scenarios like randomly, clustered non-completely and completely missing patterns, we test our proposed approach with some existing techniques such as K-Nearest Neighbor (KNN), Seasonal-Trend decomposition using Loess (STL), Bayesian tensor decomposition, Denoising AutoEncoder (DAE). The test results indicate that the hybrid method achieves the best imputation quality for most missing patterns, particularly for those with completely or hybrid missing patterns. Furthermore, the hybrid model still performs well under extreme missing rates as high as 0.9, which validates the robustness of the model in extreme situations.

随着智能交通系统（ITS）的出现，时空交通数据在交通的实时监控，预测和控制中变得越来越重要。然而，在实际的实施中，数据收集设备经常面临各种不可预测的中断所引起的故障，从而导致所谓的“价值缺失问题”。在实际情况下，数据收集设备的中断通常与一些关键事件（例如，停电和自然灾害）相关，此外，与其他中断一起，缺失值问题可能以复杂的方式随机且完全地出现。缺少模式。为了执行如此复杂的缺失模式的插补任务，我们提出了一种混合时空方法，该方法利用“先知”模型利用时间序列属性，并通过迭代随机森林模型捕获空间残差信息。时空方法首先应用时间部分来填充缺失值，然后采用空间部分来获取缺失值的残差​​分量。这两个组成部分的结果被整合到最终的估算中。基于PeMS高速公路数据集（PeMS，2019）和城市道路数据集，在广泛的人工设计场景下，例如随机，聚类，不完全和完全缺失的模式，我们使用一些现有技术（例如K最近邻（KNN））测试了我们提出的方法），使用黄土（STL）的季节性趋势分解，贝叶斯张量分解，降噪自动编码器（DAE）。测试结果表明，对于大多数缺失模式，尤其对于那些具有完全缺失模式或混合缺失模式的模式，混合方法可获得最佳插补质量。此外，混合模型在高达0.9的极端丢失率下仍然表现良好，这证明了该模型在极端情况下的鲁棒性。