Real-Time Traffic Conflict Prediction at Intersections: A Novel Approach Integrating Statistical Models and Machine Learning
Real-time traffic conflict prediction is crucial for developing proactive safety management strategies and improving overall traffic safety. However, existing studies have failed to fully consider the entire process of traffic conflict generation at both signalized and unsignalized intersections. Gi...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/atr/2239983 |
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| _version_ | 1850128838621659136 |
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| author | Chuanyun Fu Jiaming Liu Huahua Liu Xiaoli Wang Zhaoyou Lu Jushang Ou Wei Bai |
| author_facet | Chuanyun Fu Jiaming Liu Huahua Liu Xiaoli Wang Zhaoyou Lu Jushang Ou Wei Bai |
| author_sort | Chuanyun Fu |
| collection | DOAJ |
| description | Real-time traffic conflict prediction is crucial for developing proactive safety management strategies and improving overall traffic safety. However, existing studies have failed to fully consider the entire process of traffic conflict generation at both signalized and unsignalized intersections. Given this, this study proposes a real-time three-stage approach integrating statistical and machine learning models developed from three perspectives to reveal the influencing factors, occurrence identification, and quantity prediction of traffic conflicts. The results show that the proposed approach can effectively predict traffic conflicts at signalized and nonsignalized intersections. The findings of this study provide new ideas for proactive safety management in urban road networks. |
| format | Article |
| id | doaj-art-ebfb025fd06a459e84d502bd20552c3e |
| institution | OA Journals |
| issn | 2042-3195 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-ebfb025fd06a459e84d502bd20552c3e2025-08-20T02:33:09ZengWileyJournal of Advanced Transportation2042-31952025-01-01202510.1155/atr/2239983Real-Time Traffic Conflict Prediction at Intersections: A Novel Approach Integrating Statistical Models and Machine LearningChuanyun Fu0Jiaming Liu1Huahua Liu2Xiaoli Wang3Zhaoyou Lu4Jushang Ou5Wei Bai6School of Transportation Science and EngineeringSchool of Transportation Science and EngineeringSchool of Transportation Science and EngineeringJiaozhou Transportation BureauSchool of Transportation Science and EngineeringDepartment of Road Traffic ManagementDepartment of Road Traffic ManagementReal-time traffic conflict prediction is crucial for developing proactive safety management strategies and improving overall traffic safety. However, existing studies have failed to fully consider the entire process of traffic conflict generation at both signalized and unsignalized intersections. Given this, this study proposes a real-time three-stage approach integrating statistical and machine learning models developed from three perspectives to reveal the influencing factors, occurrence identification, and quantity prediction of traffic conflicts. The results show that the proposed approach can effectively predict traffic conflicts at signalized and nonsignalized intersections. The findings of this study provide new ideas for proactive safety management in urban road networks.http://dx.doi.org/10.1155/atr/2239983 |
| spellingShingle | Chuanyun Fu Jiaming Liu Huahua Liu Xiaoli Wang Zhaoyou Lu Jushang Ou Wei Bai Real-Time Traffic Conflict Prediction at Intersections: A Novel Approach Integrating Statistical Models and Machine Learning Journal of Advanced Transportation |
| title | Real-Time Traffic Conflict Prediction at Intersections: A Novel Approach Integrating Statistical Models and Machine Learning |
| title_full | Real-Time Traffic Conflict Prediction at Intersections: A Novel Approach Integrating Statistical Models and Machine Learning |
| title_fullStr | Real-Time Traffic Conflict Prediction at Intersections: A Novel Approach Integrating Statistical Models and Machine Learning |
| title_full_unstemmed | Real-Time Traffic Conflict Prediction at Intersections: A Novel Approach Integrating Statistical Models and Machine Learning |
| title_short | Real-Time Traffic Conflict Prediction at Intersections: A Novel Approach Integrating Statistical Models and Machine Learning |
| title_sort | real time traffic conflict prediction at intersections a novel approach integrating statistical models and machine learning |
| url | http://dx.doi.org/10.1155/atr/2239983 |
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