A Framework for Risk Evolution Path Forecasting Model of Maritime Traffic Accidents Based on Link Prediction
Water transportation is a critical component of the overall transportation system. However, the gradual increase in traffic density has led to a corresponding rise in accident occurrences. This study proposes a quantitative framework for analyzing the evolutionary paths of maritime traffic accident...
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| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1060 |
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| author | Shaoyong Liu Jian Deng Cheng Xie |
| author_facet | Shaoyong Liu Jian Deng Cheng Xie |
| author_sort | Shaoyong Liu |
| collection | DOAJ |
| description | Water transportation is a critical component of the overall transportation system. However, the gradual increase in traffic density has led to a corresponding rise in accident occurrences. This study proposes a quantitative framework for analyzing the evolutionary paths of maritime traffic accident risks by integrating complex network theory and link prediction methods. First, 371 maritime accident investigation reports were analyzed to identify the underlying risk factors associated with such incidents. A risk evolution network model was then constructed, within which the importance of each risk factor node was evaluated. Subsequently, several node similarity indices based on node importance were proposed. The performance of these indices was compared, and the optimal indicator was selected. This indicator was then integrated into the risk evolution network model to assess the interdependence between risk factors and accident types, ultimately identifying the most probable evolution paths from various risk factors to specific accident outcomes. The results show that the risk evolution path shows obvious characteristics: “lookout negligence” is highly correlated with collision accidents; “improper route selection” plays a critical role in the risk evolution of grounding and stranding incidents; “improper on-duty” is closely linked to sinking accidents; and “illegal operation” show a strong association with fire and explosion events. Additionally, the average risk evolution paths for collisions, groundings, and sinking accidents are relatively short, suggesting higher frequencies of occurrence for these accident types. This research provides crucial insights for managing water transportation systems and offers practical guidance for accident prevention and mitigation. |
| format | Article |
| id | doaj-art-59cd0d60cfe742a9aacd151b6456052c |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-59cd0d60cfe742a9aacd151b6456052c2025-08-20T03:27:19ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-01136106010.3390/jmse13061060A Framework for Risk Evolution Path Forecasting Model of Maritime Traffic Accidents Based on Link PredictionShaoyong Liu0Jian Deng1Cheng Xie2School of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaWater transportation is a critical component of the overall transportation system. However, the gradual increase in traffic density has led to a corresponding rise in accident occurrences. This study proposes a quantitative framework for analyzing the evolutionary paths of maritime traffic accident risks by integrating complex network theory and link prediction methods. First, 371 maritime accident investigation reports were analyzed to identify the underlying risk factors associated with such incidents. A risk evolution network model was then constructed, within which the importance of each risk factor node was evaluated. Subsequently, several node similarity indices based on node importance were proposed. The performance of these indices was compared, and the optimal indicator was selected. This indicator was then integrated into the risk evolution network model to assess the interdependence between risk factors and accident types, ultimately identifying the most probable evolution paths from various risk factors to specific accident outcomes. The results show that the risk evolution path shows obvious characteristics: “lookout negligence” is highly correlated with collision accidents; “improper route selection” plays a critical role in the risk evolution of grounding and stranding incidents; “improper on-duty” is closely linked to sinking accidents; and “illegal operation” show a strong association with fire and explosion events. Additionally, the average risk evolution paths for collisions, groundings, and sinking accidents are relatively short, suggesting higher frequencies of occurrence for these accident types. This research provides crucial insights for managing water transportation systems and offers practical guidance for accident prevention and mitigation.https://www.mdpi.com/2077-1312/13/6/1060link predictionrisk evolutioncomplex networkaccident risk path |
| spellingShingle | Shaoyong Liu Jian Deng Cheng Xie A Framework for Risk Evolution Path Forecasting Model of Maritime Traffic Accidents Based on Link Prediction Journal of Marine Science and Engineering link prediction risk evolution complex network accident risk path |
| title | A Framework for Risk Evolution Path Forecasting Model of Maritime Traffic Accidents Based on Link Prediction |
| title_full | A Framework for Risk Evolution Path Forecasting Model of Maritime Traffic Accidents Based on Link Prediction |
| title_fullStr | A Framework for Risk Evolution Path Forecasting Model of Maritime Traffic Accidents Based on Link Prediction |
| title_full_unstemmed | A Framework for Risk Evolution Path Forecasting Model of Maritime Traffic Accidents Based on Link Prediction |
| title_short | A Framework for Risk Evolution Path Forecasting Model of Maritime Traffic Accidents Based on Link Prediction |
| title_sort | framework for risk evolution path forecasting model of maritime traffic accidents based on link prediction |
| topic | link prediction risk evolution complex network accident risk path |
| url | https://www.mdpi.com/2077-1312/13/6/1060 |
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