Accurate vehicle trajectory recovery enables providing indispensable data foundations in intelligent urban transportation. However, existing methods face two challenges: i) the inability to process city-wide vehicle trajectories, and ii) the dependence on a substantial amount of accurate GPS trajectories for model training, leading to poor generalization ability. To address these issues, we propose a novel trajectory recovery system based on vehicle snapshots captured by traffic cameras, named TrajRecovery. TrajRecovery consists of three main components: i) Preprocessor processes traffic cameras and vehicle snapshots to provide necessary data for trajectory recovery; ii) Spatial Transfer Probabilistic Model (STPM) integrates road conditions and driver behavior to compute turning probability at intersections; iii) Trajectory Generator utilizes the output probabilities from STPM to recover a continuous and most likely complete trajectory. We evaluate TrajRecovery on two real datasets from a city in China, demonstrating substantial performance gains compared to state-of-the-art methods. Furthermore, our system is deployed in practical applications at Huawei Company, achieving extraordinary profits in business scenarios.

Modern industrial data is commonly dynamic and contains outliers, which challenges the accurate isolation of faulty variables in abnormal situations. To deal with process dynamics, a robust stacked probabilistic latent variable model is proposed, which is formed by stacking a series of static probabilistic latent variable models. A fault indicator matrix with the Bernoulli-Gaussian prior is constructed to indicate which process variables are faulty. The Bernoulli-Gaussian prior neatly accommodates the stacked structure of the indicator matrix so that rows corresponding to normal variables will shrink to zero. The stacked probabilistic latent variable model is further extended to deal with outliers by introducing an outlier indicator vector with the Beta-Bernoulli prior. The location and magnitude of the outliers can be successfully identified. Based on the robust stacked model, a variational Bayesian inference algorithm is developed to estimate unknown parameters. By using the piecewise affine approximation, the proposed fault isolation method can be extended to deal with nonlinear processes. The effectiveness and superiority of the method are illustrated by application studies to a simulation case and an industrial boiler case.Note to Practitioners—Data-driven fault isolation methods are critical to helping find the accurate root causes of industrial faults. While designing the fault isolation procedures, traditional data-based methods seldom consider autocorrelation and outliers characteristics in the collected process data simultaneously. This paper proposes an accurate and robust fault isolation method based on the stacked probabilistic latent variable model. For the full implementation of the model, it is necessary to: 1) construct the stacked structure of the fault indicator matrix based on the Bernoulli-Gaussian prior; 2) establish the outlier indicator vector with the Beta-Bernoulli prior to determine the location and magnitude of the outliers; 3) optimize the robust stacked model through the variational Bayesian inference algorithm with appropriately selected priors; 4) extract the faulty information of the industrial data to further enhance the faulty isolation performance. The two case studies have shown satisfactory fault-locating accuracy with the proposed model.