analiza aiqbench
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)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 237 cuvinte
Subiecte conexe
STHMoE: Hypergraph-Enhanced Heterogeneous Dependency Coordination for LLM-Based Urban Traffic Data Forecasting
arXiv · 16 sept. 2026
General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting
arXiv · 20 aug. 2026
Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing
arXiv · 6 aug. 2026
From Vessel Trajectories to Safety-Critical Encounter Scenarios: A Generative AI Framework for Autonomous Ship Digital Testing
arXiv · 4 aug. 2026