aiqbench analysis
This research introduces MambaLSTM, a novel framework leveraging state-space models (Mamba blocks) to improve traffic accident risk prediction by effectively capturing global spatial correlations and complex spatio-temporal dependencies. By addressing limitations in existing models regarding noise integration during feature fusion, the study demonstrates superior performance on real-world datasets compared to current state-of-the-art methods. The work highlights significant advancements in applying specialized machine learning architectures to critical urban safety applications.
MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction
arXiv:2607.18353v1 Announce Type: cross 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 https://github.com/Zhenzovo/MambaLSTM.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2607.18353 [cs.LG]
(or arXiv:2607.18353v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.18353 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 237 words
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