论文标题
时空交通数据的非凸低量张量完成模型
A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation
论文作者
论文摘要
从各种传感系统收集的时空交通数据中，稀疏性和缺少数据问题非常普遍。进行准确的插补对于智能运输系统中的许多应用至关重要。在本文中，我们在低级张量完成（LRTC）框架中制定了时空流量数据中缺少的数据插补问题，并在位置$ \ times $ \ times $ pay $ \ times $ \ times $ times $ times $ times $ times times time time of Lighter的交通张量上定义了一种新颖的截断核定常（TNN）。特别是，我们引入了一个通用速率参数，以控制所提出的LRTC-TNN模型中所有张量模式的截断程度，这使我们能够更好地表征时空流量数据中隐藏的模式。基于乘数交替方向方法（ADMM）的框架，我们提出了一种有效的算法，以获得每个变量的最佳解决方案。我们对四个时空交通数据集进行了数值实验，我们的结果表明，所提出的LRTC-TNN模型的表现优于许多最先进的插入模型，这些模型缺少率/模式。此外，在极端缺失的情况下，提出的模型还胜过其他基线模型。
Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems. Making accurate imputation is critical to many applications in intelligent transportation systems. In this paper, we formulate the missing data imputation problem in spatiotemporal traffic data in a low-rank tensor completion (LRTC) framework and define a novel truncated nuclear norm (TNN) on traffic tensors of location$\times$day$\times$time of day. In particular, we introduce an universal rate parameter to control the degree of truncation on all tensor modes in the proposed LRTC-TNN model, and this allows us to better characterize the hidden patterns in spatiotemporal traffic data. Based on the framework of the Alternating Direction Method of Multipliers (ADMM), we present an efficient algorithm to obtain the optimal solution for each variable. We conduct numerical experiments on four spatiotemporal traffic data sets, and our results show that the proposed LRTC-TNN model outperforms many state-of-the-art imputation models with missing rates/patterns. Moreover, the proposed model also outperforms other baseline models in extreme missing scenarios.
