Data-Driven Analysis of the Causal Chain of Waterborne Traffic Accidents: A Hybrid Framework Based on an Improved Human Factors Analysis and Classification System with a Bayesian Network
In the context of economic globalization, waterborne transportation plays an important role in international trade and logistics. However, waterborne traffic accidents pose a severe threat to life, property safety, and the environment. To gain a deeper understanding of the causal mechanisms behind w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/3/393 |
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| author | Xiangyu Yin Yan Yan Jiahao Wang Hongzhuan Zhao Qingyan Wu Qi Xu |
| author_facet | Xiangyu Yin Yan Yan Jiahao Wang Hongzhuan Zhao Qingyan Wu Qi Xu |
| author_sort | Xiangyu Yin |
| collection | DOAJ |
| description | In the context of economic globalization, waterborne transportation plays an important role in international trade and logistics. However, waterborne traffic accidents pose a severe threat to life, property safety, and the environment. To gain a deeper understanding of the causal mechanisms behind waterborne traffic accidents, we conducted a data-driven analysis of the causal chain of waterborne traffic accidents. By constructing a hybrid framework integrating an improved HFACS (Human Factors Analysis and Classification System) with a Bayesian network model, we conducted a multi-dimensional analysis of accident causes. The constructed model was quantitatively analyzed by applying genie software to the accident samples collected from the China MSA. The results indicate that there are 12, 3, 6, 2, 4, and 7 causal chains leading to collisions, contact, fires/explosions, windstorm accidents, sinking, and other types of accidents, respectively. These research results can serve as a reference for the enhancement of the safety of waterborne transportation. |
| format | Article |
| id | doaj-art-c3dfd465ad7e4b638262c5d947ee247c |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-c3dfd465ad7e4b638262c5d947ee247c2025-08-20T01:48:41ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-02-0113339310.3390/jmse13030393Data-Driven Analysis of the Causal Chain of Waterborne Traffic Accidents: A Hybrid Framework Based on an Improved Human Factors Analysis and Classification System with a Bayesian NetworkXiangyu Yin0Yan Yan1Jiahao Wang2Hongzhuan Zhao3Qingyan Wu4Qi Xu5China Waterborne Transport Research Institute, Beijing 100088, ChinaGuangxi Key Laboratory of ITS, Guilin University of Electronic Technology, Guilin 541004, ChinaGuangxi Key Laboratory of ITS, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, ChinaDepartment of Infrastructure, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, ChinaIn the context of economic globalization, waterborne transportation plays an important role in international trade and logistics. However, waterborne traffic accidents pose a severe threat to life, property safety, and the environment. To gain a deeper understanding of the causal mechanisms behind waterborne traffic accidents, we conducted a data-driven analysis of the causal chain of waterborne traffic accidents. By constructing a hybrid framework integrating an improved HFACS (Human Factors Analysis and Classification System) with a Bayesian network model, we conducted a multi-dimensional analysis of accident causes. The constructed model was quantitatively analyzed by applying genie software to the accident samples collected from the China MSA. The results indicate that there are 12, 3, 6, 2, 4, and 7 causal chains leading to collisions, contact, fires/explosions, windstorm accidents, sinking, and other types of accidents, respectively. These research results can serve as a reference for the enhancement of the safety of waterborne transportation.https://www.mdpi.com/2077-1312/13/3/393waterborne traffic accidentscausal chain analysisHFACSBayesian networkdata-driven analysis |
| spellingShingle | Xiangyu Yin Yan Yan Jiahao Wang Hongzhuan Zhao Qingyan Wu Qi Xu Data-Driven Analysis of the Causal Chain of Waterborne Traffic Accidents: A Hybrid Framework Based on an Improved Human Factors Analysis and Classification System with a Bayesian Network Journal of Marine Science and Engineering waterborne traffic accidents causal chain analysis HFACS Bayesian network data-driven analysis |
| title | Data-Driven Analysis of the Causal Chain of Waterborne Traffic Accidents: A Hybrid Framework Based on an Improved Human Factors Analysis and Classification System with a Bayesian Network |
| title_full | Data-Driven Analysis of the Causal Chain of Waterborne Traffic Accidents: A Hybrid Framework Based on an Improved Human Factors Analysis and Classification System with a Bayesian Network |
| title_fullStr | Data-Driven Analysis of the Causal Chain of Waterborne Traffic Accidents: A Hybrid Framework Based on an Improved Human Factors Analysis and Classification System with a Bayesian Network |
| title_full_unstemmed | Data-Driven Analysis of the Causal Chain of Waterborne Traffic Accidents: A Hybrid Framework Based on an Improved Human Factors Analysis and Classification System with a Bayesian Network |
| title_short | Data-Driven Analysis of the Causal Chain of Waterborne Traffic Accidents: A Hybrid Framework Based on an Improved Human Factors Analysis and Classification System with a Bayesian Network |
| title_sort | data driven analysis of the causal chain of waterborne traffic accidents a hybrid framework based on an improved human factors analysis and classification system with a bayesian network |
| topic | waterborne traffic accidents causal chain analysis HFACS Bayesian network data-driven analysis |
| url | https://www.mdpi.com/2077-1312/13/3/393 |
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