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

Abstract

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) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data sparsity</i> . 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) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data distribution mutation</i> . 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.