GMAT-DU: Traffic Anomaly Prediction With Fine Spatiotemporal Granularity in Sparse Data

The fine-grained prediction of traffic anomalies is crucial for Traffic Management Bureau to alleviate congestion and avoid public safety incidents. While in practice, the fine-grained prediction is very challenging due to two issues. 1) Data sparsity. At the fine-grained setting, missing data is inevitable and widespread on spatial and temporal dimension. Existing methods have weak performance as they do not handle missing data properly. 2) Data distribution mutation. At the fine-grained setting, the traffic conditions of adjacent road segments are sometimes completely different, invalidating existing spatiotemporal smoothing-based methods. This paper proposes GMAT-DU, a novel model that aims to predict traffic anomaly from sparse data in fine-grained manner. To solve the first issue, we propose a Decay Unrolling (DU) mechanism to make the model applicable to sparse datasets. The performance will be progressively enhanced by the spatiotemporal unrolling of high-impact neighbors. For the second issue, we combine the meta-features of roads with correlations between roads, which are learnt from road semantic information and historical spatiotemporal data, and make the model focusing on the high-impact neighbors by a Graph Meta-features based ATtention (GMAT) mechanism. Extensive experiments on two real-world datasets validate the effectiveness of our method. The experiment results show the significant advantages against the state-of-the-art models.