Real-time prediction model of public safety events driven by multi-source heterogeneous data
To address the challenge of efficiently integrating multi-source heterogeneous data to improve the accuracy of public safety event prediction, this study proposes and validates a novel public safety event prediction model, GATPNet, based on multi-source heterogeneous data. The model integrates Graph...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1553640/full |
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| author | Quanlong Fan Gang Xu |
| author_facet | Quanlong Fan Gang Xu |
| author_sort | Quanlong Fan |
| collection | DOAJ |
| description | To address the challenge of efficiently integrating multi-source heterogeneous data to improve the accuracy of public safety event prediction, this study proposes and validates a novel public safety event prediction model, GATPNet, based on multi-source heterogeneous data. The model integrates Graph Attention Networks (GAT), Spatiotemporal Transformers, and Proximal Policy Optimization (PPO) to achieve effective data fusion, spatiotemporal feature extraction, and real-time decision support. Through experiments conducted on the Los Angeles Crime Data and CrisisLexT26 datasets, this study demonstrates that GATPNet outperforms other baseline models. On the Los Angeles Crime Data dataset, GATPNet achieved an accuracy of 90%, recall of 89%, Spatiotemporal Prediction Accuracy (STPA) of 80%, and a response time of 1.9 s, showing a 5% improvement in accuracy and a 10% improvement in STPA over the best baseline method. On the CrisisLexT26 dataset, it achieved an accuracy of 89%, recall of 88%, STPA of 78%, and a response time of 2.1 s, showing a 4% improvement in accuracy and a 6% improvement in STPA over the best baseline method. Additionally, ablation experiments further indicate that each module plays a critical role in improving overall performance. Despite the model’s high computational complexity when handling large-scale heterogeneous data and the limited coverage of the datasets, GATPNet still demonstrates its broad application potential in public safety event prediction and management, offering effective technical support for social governance and emergency management. |
| format | Article |
| id | doaj-art-4bc7657bf1e04e2fa29c9fcea56fceea |
| institution | DOAJ |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-4bc7657bf1e04e2fa29c9fcea56fceea2025-08-20T03:04:11ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-04-011310.3389/fphy.2025.15536401553640Real-time prediction model of public safety events driven by multi-source heterogeneous dataQuanlong Fan0Gang Xu1Zhejiang Cheng’an Big Data Co., Ltd., Wenzhou, ChinaFaculty of Artificial Intelligence, Zhejiang Institute of Security Vocational Technology, Wenzhou, ChinaTo address the challenge of efficiently integrating multi-source heterogeneous data to improve the accuracy of public safety event prediction, this study proposes and validates a novel public safety event prediction model, GATPNet, based on multi-source heterogeneous data. The model integrates Graph Attention Networks (GAT), Spatiotemporal Transformers, and Proximal Policy Optimization (PPO) to achieve effective data fusion, spatiotemporal feature extraction, and real-time decision support. Through experiments conducted on the Los Angeles Crime Data and CrisisLexT26 datasets, this study demonstrates that GATPNet outperforms other baseline models. On the Los Angeles Crime Data dataset, GATPNet achieved an accuracy of 90%, recall of 89%, Spatiotemporal Prediction Accuracy (STPA) of 80%, and a response time of 1.9 s, showing a 5% improvement in accuracy and a 10% improvement in STPA over the best baseline method. On the CrisisLexT26 dataset, it achieved an accuracy of 89%, recall of 88%, STPA of 78%, and a response time of 2.1 s, showing a 4% improvement in accuracy and a 6% improvement in STPA over the best baseline method. Additionally, ablation experiments further indicate that each module plays a critical role in improving overall performance. Despite the model’s high computational complexity when handling large-scale heterogeneous data and the limited coverage of the datasets, GATPNet still demonstrates its broad application potential in public safety event prediction and management, offering effective technical support for social governance and emergency management.https://www.frontiersin.org/articles/10.3389/fphy.2025.1553640/fullpublic safety eventdeep learningreal-time predictionmulti-source data fusiongraph neural networks (GNN)data integration |
| spellingShingle | Quanlong Fan Gang Xu Real-time prediction model of public safety events driven by multi-source heterogeneous data Frontiers in Physics public safety event deep learning real-time prediction multi-source data fusion graph neural networks (GNN) data integration |
| title | Real-time prediction model of public safety events driven by multi-source heterogeneous data |
| title_full | Real-time prediction model of public safety events driven by multi-source heterogeneous data |
| title_fullStr | Real-time prediction model of public safety events driven by multi-source heterogeneous data |
| title_full_unstemmed | Real-time prediction model of public safety events driven by multi-source heterogeneous data |
| title_short | Real-time prediction model of public safety events driven by multi-source heterogeneous data |
| title_sort | real time prediction model of public safety events driven by multi source heterogeneous data |
| topic | public safety event deep learning real-time prediction multi-source data fusion graph neural networks (GNN) data integration |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2025.1553640/full |
| work_keys_str_mv | AT quanlongfan realtimepredictionmodelofpublicsafetyeventsdrivenbymultisourceheterogeneousdata AT gangxu realtimepredictionmodelofpublicsafetyeventsdrivenbymultisourceheterogeneousdata |