Accident Data-Driven Consequence Analysis in Maritime Industries
Maritime accidents are significant obstacles to the development of shipping industries. Their consequences are another important issue because they often involve significant economic losses and human casualties. Accident consequences do not occur randomly, but are triggered by a series of influentia...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/1/117 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588183633657856 |
|---|---|
| author | Jiahui Shi Zhengjiang Liu |
| author_facet | Jiahui Shi Zhengjiang Liu |
| author_sort | Jiahui Shi |
| collection | DOAJ |
| description | Maritime accidents are significant obstacles to the development of shipping industries. Their consequences are another important issue because they often involve significant economic losses and human casualties. Accident consequences do not occur randomly, but are triggered by a series of influential factors. To determine the critical factors contributing to accident consequences, a data-driven research framework is proposed. Firstly, 198 maritime accident investigation reports from the Marine Accident Investigation Branch (MAIB) and Australian Transport Safety Bureau (ATSB) are collected to build a database. Secondly, relevant influential factors are identified based on a literature review. Thirdly, a TAN (Tree Augmented Network)-based BN (Bayesian network) model is developed. Fourthly, a model validation process, including a comparative analysis, Kappa test, and scenario analysis are performed. The five critical factors are determined as accident type, ship type, ship age, ship length and gross tonnage. Valuable implications are generated through this research framework and can be a valuable reference for the safety management of concerned parties. In addition, the TAN model can be a predictor for developing mitigation measures to minimize accident consequences. |
| format | Article |
| id | doaj-art-1d3b9c92552f407db9226f81a6099fc6 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-1d3b9c92552f407db9226f81a6099fc62025-01-24T13:36:55ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113111710.3390/jmse13010117Accident Data-Driven Consequence Analysis in Maritime IndustriesJiahui Shi0Zhengjiang Liu1Navigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaMaritime accidents are significant obstacles to the development of shipping industries. Their consequences are another important issue because they often involve significant economic losses and human casualties. Accident consequences do not occur randomly, but are triggered by a series of influential factors. To determine the critical factors contributing to accident consequences, a data-driven research framework is proposed. Firstly, 198 maritime accident investigation reports from the Marine Accident Investigation Branch (MAIB) and Australian Transport Safety Bureau (ATSB) are collected to build a database. Secondly, relevant influential factors are identified based on a literature review. Thirdly, a TAN (Tree Augmented Network)-based BN (Bayesian network) model is developed. Fourthly, a model validation process, including a comparative analysis, Kappa test, and scenario analysis are performed. The five critical factors are determined as accident type, ship type, ship age, ship length and gross tonnage. Valuable implications are generated through this research framework and can be a valuable reference for the safety management of concerned parties. In addition, the TAN model can be a predictor for developing mitigation measures to minimize accident consequences.https://www.mdpi.com/2077-1312/13/1/117maritime accidentaccident consequencesTANmaritime safety |
| spellingShingle | Jiahui Shi Zhengjiang Liu Accident Data-Driven Consequence Analysis in Maritime Industries Journal of Marine Science and Engineering maritime accident accident consequences TAN maritime safety |
| title | Accident Data-Driven Consequence Analysis in Maritime Industries |
| title_full | Accident Data-Driven Consequence Analysis in Maritime Industries |
| title_fullStr | Accident Data-Driven Consequence Analysis in Maritime Industries |
| title_full_unstemmed | Accident Data-Driven Consequence Analysis in Maritime Industries |
| title_short | Accident Data-Driven Consequence Analysis in Maritime Industries |
| title_sort | accident data driven consequence analysis in maritime industries |
| topic | maritime accident accident consequences TAN maritime safety |
| url | https://www.mdpi.com/2077-1312/13/1/117 |
| work_keys_str_mv | AT jiahuishi accidentdatadrivenconsequenceanalysisinmaritimeindustries AT zhengjiangliu accidentdatadrivenconsequenceanalysisinmaritimeindustries |