Missing data is an inevitable and common problem in data-driven intelligent transportation systems (ITS). In the past decade, scholars have done many research on the recovery of missing traffic data, however how to make full use of spatio-temporal traffic patterns to improve the recovery performance is still an open problem. Aiming at the spatio-temporal characteristics of traffic speed data, this paper regards the recovery of missing data as a matrix completion problem, and proposes a spatio-temporal traffic data completion method based on hidden feature analysis, which discovers spatio-temporal patterns and underlying structures from incomplete data to complete the recovery task. Therefore, we introduce spatial and temporal correlation to capture the main underlying features of each dimension. Finally, these latent features are applied to recovery traffic data through latent feature analysis. The experimental and evaluation results show that the evaluation criterion value of the model is small, which indicates that the model has better performance. The results show that the model can accurately estimate the continuous missing data.
翻译:在数据驱动的智能运输系统(ITS)中,缺少的数据是一个不可避免的常见问题。在过去的十年中,学者们对恢复缺失的交通数据进行了许多研究,然而,如何充分利用时空通信模式来改善恢复性能仍然是一个尚未解决的问题。本文件着眼于交通速度数据的时空特征,将恢复缺失的数据视为一个矩阵完成问题,并提议基于隐藏特征分析的时空数据完成方法,该方法从不完整的数据中发现时空模式和基本结构,以完成恢复任务。因此,我们引入了空间和时间相关性,以捕捉每个方面的主要基本特征。最后,这些潜在特征通过潜在特征分析用于恢复交通数据。实验和评价结果显示,模型的评价标准值很小,表明模型的性能更好。结果显示,模型可以准确估计持续缺失的数据。