Robust Tensor Recovery with Fiber Outliers for Traffic Events

by   Yue Hu, et al.

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 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.


page 17

page 18


A Parameter-free Nonconvex Low-rank Tensor Completion Model for Spatiotemporal Traffic Data Recovery

Traffic data chronically suffer from missing and corruption, leading to ...

Streaming data preprocessing via online tensor recovery for large environmental sensor networks

Measuring the built and natural environment at a fine-grained scale is n...

Adapting to Skew: Imputing Spatiotemporal Urban Data with 3D Partial Convolutions and Biased Masking

We adapt image inpainting techniques to impute large, irregular missing ...

Tensor completion using geodesics on Segre manifolds

We propose a Riemannian conjugate gradient (CG) optimization method for ...

A Parallelizable Optimization Method for Missing Internet Traffic Tensor Data

Recovery of internet network traffic data from incomplete observed data ...

A Parallelizable Method for Missing Internet Traffic Tensor Data

Recovery of internet network traffic data from incomplete observed data ...

Please sign up or login with your details

Forgot password? Click here to reset