Event detection is gaining increasing attention in smart cities research. Large-scale mobility data serves as an important tool to uncover the dynamics of urban transportation systems, and more often than not the dataset is incomplete. In this article, we develop a method to detect extreme events in large traffic datasets, and to impute missing data during regular conditions. Specifically, we propose a robust tensor recovery problem to recover low-rank tensors under fiber-sparse corruptions with partial observations, and use it to identify events, and impute missing data under typical conditions. Our approach is scalable to large urban areas, taking full advantage of the spatio-temporal correlations in traffic patterns. We develop an efficient algorithm to solve the tensor recovery problem based on the alternating direction method of multipliers (ADMM) framework. Compared with existing l 1 norm regularized tensor decomposition methods, our algorithm can exactly recover the values of uncorrupted fibers of a low-rank tensor and find the positions of corrupted fibers under mild conditions. Numerical experiments illustrate that our algorithm can achieve exact recovery and outlier detection even with missing data rates as high as 40% under 5% gross corruption, depending on the tensor size and the Tucker rank of the low rank tensor. Finally, we apply our method on a real traffic dataset corresponding to downtown Nashville, TN and successfully detect the events like severe car crashes, construction lane closures, and other large events that cause significant traffic disruptions.

中文翻译：

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使用光纤异常值对交通事件进行稳健的张量恢复

事件检测在智慧城市研究中越来越受到关注。大规模移动数据是揭示城市交通系统动态的重要工具，而且数据集往往不完整。在本文中，我们开发了一种方法来检测大型交通数据集中的极端事件，并在常规条件下估算缺失数据。具体来说，我们提出了一个鲁棒的张量恢复问题，通过部分观察来恢复纤维稀疏损坏下的低秩张量，并用它来识别事件，并在典型条件下估算缺失数据。我们的方法可扩展到大城市地区，充分利用交通模式的时空相关性。我们开发了一种有效的算法来解决基于乘法器的交替方向法(ADMM) 框架。与现有相比l 1范数正则化张量分解方法，我们的算法可以准确地恢复低秩张量的未损坏纤维的值，并在温和条件下找到损坏纤维的位置。数值实验表明，我们的算法可以实现精确的恢复和异常值检测，即使在 5% 的严重损坏下丢失数据率高达 40%，这取决于张量大小和低秩张量的 Tucker 秩。最后，我们将我们的方法应用于与田纳西州纳什维尔市中心相对应的真实交通数据集，并成功检测到严重车祸、施工车道关闭和其他导致严重交通中断的大型事件等事件。