MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction
Summary
The paper proposes MambaLSTM, a framework combining Mamba state-space models and LSTM for spatio-temporal traffic accident risk prediction, addressing noise in feature fusion and global spatial correlation.
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Cached at: 07/22/26, 08:20 AM
# MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction Source: [https://arxiv.org/abs/2607.18353](https://arxiv.org/abs/2607.18353) [View PDF](https://arxiv.org/pdf/2607.18353) > Abstract: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[this https URL](https://github.com/Zhenzovo/MambaLSTM)\. ## Submission history From: Zhen Yu \[[view email](https://arxiv.org/show-email/428440b6/2607.18353)\] **\[v1\]**Mon, 20 Jul 2026 09:17:58 UTC \(777 KB\)
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