Region feature enhancement and multi-view diffusion graph convolutional network for traffic accident risk prediction.
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| Title: | Region feature enhancement and multi-view diffusion graph convolutional network for traffic accident risk prediction. |
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| Authors: | Zong, Xinlu1 (AUTHOR) zongxinlu@126.com, Guo, Jiawei1 (AUTHOR) guojiawei130@163.com, Dong, Siyu1 (AUTHOR) dsyqqk@163.com |
| Source: | Applied Intelligence. Apr2026, Vol. 56 Issue 5, p1-22. 22p. |
| Abstract: | Traffic accident prediction is a crucial component of Intelligent Transportation Systems (ITS), playing a vital role in reducing accident rates and enhancing urban traffic safety. However, accurately forecasting accident risk remains challenging due to the complex spatial–temporal dependencies and multiple external influencing factors. To address this issue, this study proposes a Region Feature Enhancement and Multi-view Diffusion Graph Convolutional Network (RFE-MDGCN) for traffic accident risk prediction. The model introduces a Region Feature Enhancement (RFE) module that employs channel-level and spatial-level attention mechanisms to capture spatial dependencies and highlight region-specific risk features. A Multi-view Diffusion Graph Convolutional Network is further designed to model similarity and semantic relationships among urban regions through a graph diffusion process, thereby enriching node representations with high-order neighbor information. To capture temporal dependencies, a Spatial–Temporal Fusion module integrating a Long Short-Term Memory–Transformer Encoder (LT Encoder) is developed to dynamically learn both short-term continuity and long-term periodicity of accident risk. Experimental results on two real-world datasets, New York City (NYC) and Chicago (ZJG), show that the proposed method achieves improvements of 11.45% in Recall and 16.10% in Mean Average Precision (MAP) over state-of-the-art baselines, demonstrating its robustness and effectiveness for traffic accident risk prediction. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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