用MambaLSTM提升交通事故风险预测，兼顾时空完整性与全局关联。
MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction
- 设计时序特征融合模块，避免时空信息失真。
- 引入Mamba块建模城市区域全局语义，提升空间相关性捕捉能力。
- 适合交通管理、智能驾驶等需高精度风险预警的场景。
在交通事故风险预测中，现有研究常忽视时序特征融合带来的额外噪声，且难以捕捉空间区域间的全局相关性。为此，我们提出新型框架MambaLSTM。首先，设计压缩-激励时序特征融合模块，在不破坏时空一致性的前提下整合时序信息。其次，引入新嵌入模块以有效捕捉空间相邻区域间的语义关系。此外，基于状态空间模型构建Mamba块，用于建模城市区域的全局空间语义。最后，提出MambaLSTM单元，高效捕获长短期时序依赖，识别动态风险模式。在真实数据集上的大量实验表明，该模型优于当前最优方法。代码已开源：https://github.com/Zhenzovo/MambaLSTM。
原文摘要 · Abstract (English)
In traffic accident risk prediction, most studies overlook the extra noise that could be incorporated when fusing temporal features into spatial features, and some models struggle to capture global correlations among spatial regions. To address these challenges, we propose a novel traffic accident risk prediction framework named MambaLSTM. First, we develop a squeeze-and-excitation temporal feature fusion module to integrate temporal information without compromising spatio-temporal integrity. Second, we introduce a new patch embedding module for effectively capturing semantic relationships among spatially adjacent regions. Additionally, we introduce a Mamba block based on state-space models to model global spatial semantics in urban regions. Finally, we propose a MambaLSTM unit to efficiently capture long- and short-term temporal dependencies for identifying dynamic risk patterns. Extensive experiments on real-world datasets demonstrate the proposed model's superiority over state-of-the-art methods. The code is released at https://github.com/Zhenzovo/MambaLSTM.
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