基于纤维异常值的交通事件鲁棒张量恢复
Robust Tensor Recovery with Fiber Outliers for Traffic Events
摘要：
事件检测在智慧城市研究中越来越受到关注。大规模的移动数据是揭示城市交通系统动态的重要工具，而数据集往往是不完整的。在这篇文章中，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，我们提出了一个强大的张量恢复问题，以恢复低秩张量下的纤维稀疏腐败与部分观测，并使用它来识别事件，并在典型条件下的缺失数据。我们的方法是可扩展的大城市地区，充分利用交通模式的时空相关性。我们开发了一个有效的算法来解决张量恢复问题的交替方向方法的乘数（ADMM）框架的基础上。与现有的$l_1$范数正则化张量分解方法相比，该算法能够在较温和的条件下准确地恢复低秩张量的未受损纤维的值，并找到受损纤维的位置.数值实验表明，该算法在数据丢失率高达40%的情况下，仍能准确地检测出异常点，条件是异常点损坏率和低秩张量的Tucker秩.最后，我们将我们的方法应用于与美国田纳西州纳什维尔市中心相对应的真实交通数据集，并成功地检测到严重车祸，施工车道关闭以及其他导致重大交通中断的大型事件。
英文摘要：
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 exactly detect outliers even with missing data rates as high as 40%, conditioned on the outlier corruption rate and the Tucker rank of the low rank tensor. Finally, we apply our method on a real traffic dataset corresponding to downtown Nashville, TN, USA and successfully detect the events like severe car crashes, construction lane closures, and other large events that cause significant traffic disruptions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
