Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories.
With the acceleration of urbanization and the increase in traffic volume, frequent traffic accidents have significantly impacted public safety and socio-economic conditions. Traditional methods for predicting traffic accidents often overlook spatiotemporal features and the complexity of traffic netw...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0320656 |
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| _version_ | 1850187963114192896 |
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| author | Hao Li Linbing Chen |
| author_facet | Hao Li Linbing Chen |
| author_sort | Hao Li |
| collection | DOAJ |
| description | With the acceleration of urbanization and the increase in traffic volume, frequent traffic accidents have significantly impacted public safety and socio-economic conditions. Traditional methods for predicting traffic accidents often overlook spatiotemporal features and the complexity of traffic networks, leading to insufficient prediction accuracy in complex traffic environments. To address this, this paper proposes a deep learning model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Graph Neural Networks (GNN) for traffic accident risk prediction using vehicle spatiotemporal trajectory data. The model extracts spatial features such as vehicle speed, acceleration, and lane-changing distance through CNN, captures temporal dependencies in trajectories using LSTM, and effectively models the complex spatial structure of traffic networks with GNN, thereby improving prediction accuracy.The main contributions of this paper are as follows: First, an innovative combined model is proposed, which comprehensively considers spatiotemporal features and road network relationships, significantly improving prediction accuracy. Second, the model's strong generalization ability across multiple traffic scenarios is validated, enhancing the accuracy of traditional prediction methods. Finally, a new technical approach is provided, offering theoretical support for the implementation of real-time traffic accident warning systems. Experimental results demonstrate that the model can effectively predict accident risks in various complex traffic scenarios, providing robust support for intelligent traffic management and public safety. |
| format | Article |
| id | doaj-art-4d89c1ee6cd24e34addc46558a577611 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-4d89c1ee6cd24e34addc46558a5776112025-08-20T02:15:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032065610.1371/journal.pone.0320656Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories.Hao LiLinbing ChenWith the acceleration of urbanization and the increase in traffic volume, frequent traffic accidents have significantly impacted public safety and socio-economic conditions. Traditional methods for predicting traffic accidents often overlook spatiotemporal features and the complexity of traffic networks, leading to insufficient prediction accuracy in complex traffic environments. To address this, this paper proposes a deep learning model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Graph Neural Networks (GNN) for traffic accident risk prediction using vehicle spatiotemporal trajectory data. The model extracts spatial features such as vehicle speed, acceleration, and lane-changing distance through CNN, captures temporal dependencies in trajectories using LSTM, and effectively models the complex spatial structure of traffic networks with GNN, thereby improving prediction accuracy.The main contributions of this paper are as follows: First, an innovative combined model is proposed, which comprehensively considers spatiotemporal features and road network relationships, significantly improving prediction accuracy. Second, the model's strong generalization ability across multiple traffic scenarios is validated, enhancing the accuracy of traditional prediction methods. Finally, a new technical approach is provided, offering theoretical support for the implementation of real-time traffic accident warning systems. Experimental results demonstrate that the model can effectively predict accident risks in various complex traffic scenarios, providing robust support for intelligent traffic management and public safety.https://doi.org/10.1371/journal.pone.0320656 |
| spellingShingle | Hao Li Linbing Chen Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories. PLoS ONE |
| title | Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories. |
| title_full | Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories. |
| title_fullStr | Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories. |
| title_full_unstemmed | Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories. |
| title_short | Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories. |
| title_sort | traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories |
| url | https://doi.org/10.1371/journal.pone.0320656 |
| work_keys_str_mv | AT haoli trafficaccidentriskpredictionbasedondeeplearningandspatiotemporalfeaturesofvehicletrajectories AT linbingchen trafficaccidentriskpredictionbasedondeeplearningandspatiotemporalfeaturesofvehicletrajectories |