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] |
| Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192221330 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Region feature enhancement and multi-view diffusion graph convolutional network for traffic accident risk prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zong%2C+Xinlu%22">Zong, Xinlu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zongxinlu@126.com</i><br /><searchLink fieldCode="AR" term="%22Guo%2C+Jiawei%22">Guo, Jiawei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> guojiawei130@163.com</i><br /><searchLink fieldCode="AR" term="%22Dong%2C+Siyu%22">Dong, Siyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dsyqqk@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>. Apr2026, Vol. 56 Issue 5, p1-22. 22p. – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10489-026-07140-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Titles: – TitleFull: Region feature enhancement and multi-view diffusion graph convolutional network for traffic accident risk prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zong, Xinlu – PersonEntity: Name: NameFull: Guo, Jiawei – PersonEntity: Name: NameFull: Dong, Siyu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 56 – Type: issue Value: 5 Titles: – TitleFull: Applied Intelligence Type: main |
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