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.

Urban flow prediction is a fundamental task to build smart cities, where neural networks have become the most popular method. However, the deep learning methods typically rely on massive training data that are probably inaccessible in real world. In light of this, the community calls for knowledge transfer. However, when adapting transfer learning for cross-city prediction tasks, existing studies are built on static knowledge transfer, ignoring the fact inter-city correlations change dynamically across time. The dynamic correlations make urban feature transfer challenging. This paper proposes a novel Spatio-Temporal Adaptation Network (STAN) to perform urban flow prediction for data-scarce cities via the spatio-temporal knowledge transferred from data-rich cities. STAN encompasses three modules: i) spatial adversarial adaptation module that adopts an adversarial manner to capture the transferable spatial features; ii) temporal attentive adaptation module to attend to critical dynamics for temporal feature transfer; iii) prediction module that aims to learn task-driven transferable knowledge. Extensive experiments on five real datasets show STAN substantially outperforms state-of-the-art methods.