用多尺度图注意力和循环模型预测交通事故，提升准确性和可解释性。
MSGAT-GRU: A Multi-Scale Graph Attention and Recurrent Model for Spatiotemporal Road Accident Prediction
- 融合交通流、天气等多源数据，捕捉局部与长程空间依赖
- 在混合北京数据集上RMSE达0.334，F1-score为0.878，优于基线
- 模型可迁移至其他城市，适合智能交通与道路安全分析
由于城市交通中空间、时间与情境因素交织，精准预测交通事故仍具挑战。本文提出MSGAT-GRU，一种多尺度图注意力与循环结合的模型，联合捕捉局部与长程空间依赖，并建模序列动态。异构输入如交通流、道路属性、天气及兴趣点被系统融合，以增强鲁棒性与可解释性。在混合北京事故数据集上，MSGAT-GRU实现RMSE 0.334、F1-score 0.878，持续优于强基线。在METR-LA数据集上进行1小时时域跨数据集评估，其RMSE为6.48（对比GMAN模型的7.21），MAPE表现相当。消融实验表明，三跳空间聚合与双层GRU在精度与稳定性间达到最佳平衡。结果表明，MSGAT-GRU是智能交通系统中可扩展、通用性强的模型，能提供可解释信号，支持主动交通管理与道路安全分析。
原文摘要 · Abstract (English)
Accurate prediction of road accidents remains challenging due to intertwined spatial, temporal, and contextual factors in urban traffic. We propose MSGAT-GRU, a multi-scale graph attention and recurrent model that jointly captures localized and long-range spatial dependencies while modeling sequential dynamics. Heterogeneous inputs, such as traffic flow, road attributes, weather, and points of interest, are systematically fused to enhance robustness and interpretability. On the Hybrid Beijing Accidents dataset, MSGAT-GRU achieves an RMSE of 0.334 and an F1-score of 0.878, consistently outperforming strong baselines. Cross-dataset evaluation on METR-LA under a 1-hour horizon further supports transferability, with RMSE of 6.48 (vs. 7.21 for the GMAN model) and comparable MAPE. Ablations indicate that three-hop spatial aggregation and a two-layer GRU offer the best accuracy-stability trade-off. These results position MSGAT-GRU as a scalable and generalizable model for intelligent transportation systems, providing interpretable signals that can inform proactive traffic management and road safety analytics.
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