TA-STAN: A Deep Spatial-Temporal Attention Learning Framework for Regional Traffic Accident Risk Prediction

Lei Zhu, Tianrui Li, Shengdong Du

Abstract: Accurate and effective prediction of future traffic accident risk is critical to reducing the number of traffic accidents, which is also of great help to personal safe travel. In our paper, we choose the real traffic administrative area as the way of regional division rather than grid map, so that our prediction results can be applied to true traffic scenarios directly. Instead of considering traffic flow as a single factor affecting traffic accidents, we divide traffic flow into multiple traffic volumes based on vehicle type. In order to better model the dynamic impact of different traffic flow data and traffic accident data in the local region and global regions for future traffic accident risk prediction, we design a deep learning framework to predict regional Traffic Accident risk that utilizes a Spatial-Temporal Attention Network (named TA-STAN). We also integrate many external environmental factors to further improve the accuracy. We evaluate our TA-STAN model on the real traffic accident dataset in New York City. The experimental results show that TA-STAN outperforms 6 baseline models in 3 evaluation metrics. More importantly, by visualizing the weight of attention, we can reasonably interpret the actual meaning of attention weights, which plays a crucial role in our model.