AI 中文总结
该研究针对交通事故风险预测中存在的问题，提出MambaLSTM框架，通过开发挤压激励时间特征融合模块、新补丁嵌入模块、引入Mamba块及MambaLSTM单元，有效捕捉时空特征与依赖性来识别风险模式，实验表明其优于现有方法。
AI 中文摘要
在交通事故风险预测中，多数研究在将时间特征融合到空间特征时忽略了可能纳入的额外噪声，一些模型难以捕捉空间区域间的全局相关性。为应对这些挑战，我们提出了名为MambaLSTM的新型交通事故风险预测框架。首先，开发了挤压激励时间特征融合模块以整合时间信息且不损害时空完整性。其次，引入新的补丁嵌入模块有效捕捉空间相邻区域间的语义关系。此外，基于状态空间模型引入Mamba块来建模城市区域的全局空间语义。最后，提出MambaLSTM单元有效捕捉长期和短期时间依赖性以识别动态风险模式。在真实世界数据集上的大量实验证明了该模型优于现有方法。代码已在指定网址发布。
英文摘要
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.
