@ARTICLE{Feng_Jian_Traffic_Early,
 author={Feng, Jian and Liu, Tianchao and Qiao, Yuqiang},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e151955},
 howpublished={online},
 year={Early Access},
 abstract={Traffic accident prediction is a crucial component of the Intelligent Traffic System, which is important to maintain citizen safety and decrease economic losses. Current deep learning based methods for traffic accident prediction fail to consider the driving mechanism of traffic accidents, so a novel traffic accident prediction method based on multi-view spatial-temporal learning is proposed, which represents the driving mechanism of traffic accidents from multiple views. Firstly, for the urban regions divided by grids, a new augmentation has been designed to augment the spatial semantic information of regions through learnable semantic embedding, then Deformable Convolutional Networks with non-fixed convolution kernels are used to learn dynamic spatial dependencies between regions and Gated Recurrent Units are used to learn temporal dependencies, which can capture dynamic spatial-temporal evolution patterns of traffic accidents. Secondly, Long Short-Term Memory is employed to learn the traffic flow breakdown from the flow difference of adjacent time steps in each region to recognize the traffic accident precursor in the risk environment. Thirdly, accident patterns in different regions are learned from historical traffic flow to determine whether the flow is the dominant factor and capture the spatial heterogeneity of traffic accidents. Finally, the above features are fused for accident prediction at the regional level. Experiments are conducted on 2 real datasets, and the experimental results show that the proposed method outperforms 8 benchmark methods},
 type={Article},
 title={Traffic accident prediction method based on multi-view spatial-temporal learning},
 URL={http://journals.pan.pl/Content/132446/PDF/BPASTS-04235-EA.pdf},
 doi={10.24425/bpasts.2024.151955},
 keywords={traffic accident prediction, spatial-temporal dependencies, accident precursor, spatial heterogeneity},
}