Robust Tensor Recovery with Fiber Outliers for Traffic Events
Robust Tensor Recovery with Fiber Outliers for Traffic Events
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DOI：
10.1145/3417337
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发表时间：
2019-08
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影响因子：
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通讯作者：
Yue Hu;D. Work
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文献类型：
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作者：
Yue Hu;D. Work
事件检测在智慧城市研究中受到越来越多的关注。大规模移动数据是揭示城市交通系统动态的重要工具，而数据集往往不完整。在本文中，我们开发了一种在大型交通数据集中检测极端事件以及在常规情况下填补缺失数据的方法。具体而言，我们提出了一个稳健的张量恢复问题，以便在部分观测的情况下，在纤维稀疏损坏的情况下恢复低秩张量，并利用它来识别事件以及在典型条件下填补缺失数据。我们的方法可扩展到大型城市区域，充分利用了交通模式中的时空相关性。我们基于交替方向乘子法（ADMM）框架开发了一种有效的算法来解决张量恢复问题。与现有的l1范数正则化张量分解方法相比，我们的算法在温和条件下能够准确恢复低秩张量未损坏纤维的值，并找到损坏纤维的位置。数值实验表明，根据张量大小和低秩张量的塔克秩，即使在总损坏率为5%的情况下缺失数据率高达40%，我们的算法也能够实现精确恢复和异常值检测。最后，我们将我们的方法应用于田纳西州纳什维尔市中心的一个真实交通数据集，并成功检测到诸如严重车祸、施工车道封闭以及其他导致重大交通混乱的大型事件。
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 l1 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.