IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v257y2025ipas0951832025000079.html
   My bibliography  Save this article

Graph neural networks enabled accident causation prediction for maritime vessel traffic

Author

Listed:
  • Gan, Langxiong
  • Gao, Ziyi
  • Zhang, Xiyu
  • Xu, Yi
  • Liu, Ryan Wen
  • Xie, Cheng
  • Shu, Yaqing

Abstract

Maritime vessel traffic accidents frequently result in significant casualties, property damage, and environmental pollution. The investigation of these accidents is crucial for determining responsibility and improving navigation rules. However, the complexity of the marine environment complicates the quick identification of accident factors by maritime authorities. In this study, the causes of maritime vessel traffic accidents were predicted. Firstly, 501 maritime vessel traffic accident investigation reports published on the official website of the China Maritime Safety Administration were collected and analyzed. Secondly, a maritime vessel traffic accident causation model was constructed, and information from the accident reports was extracted based on the model. Finally, textual information was converted into graph data, and a Mutual Information-based Deep Graph Convolutional Network (GCN) model (MIDG-GCN) was proposed to analyze accident characteristics and predict causation. A ranked list of accident causes was generated based on scores, demonstrating favorable comparisons with commonly used Graph Neural Networks (GNNs). The MeanRank of the model was found to be 9.5436, indicating effectiveness in ranking predicted causation. High accuracy in predicting the top-3 and top-10 causes was shown by Hits@3 and Hits@10 scores of 0.7505 and 0.9701, respectively. The proposed model provides valuable causation predictions to guide decision-making in accident investigations.

Suggested Citation

  • Gan, Langxiong & Gao, Ziyi & Zhang, Xiyu & Xu, Yi & Liu, Ryan Wen & Xie, Cheng & Shu, Yaqing, 2025. "Graph neural networks enabled accident causation prediction for maritime vessel traffic," Reliability Engineering and System Safety, Elsevier, vol. 257(PA).
    • Handle: RePEc:eee:reensy:v:257:y:2025:i:pa:s0951832025000079
    DOI: 10.1016/j.ress.2025.110804
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832025000079
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2025.110804?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lan, He & Ma, Xiaoxue & Ma, Laihao & Qiao, Weiliang, 2023. "Pattern investigation of total loss maritime accidents based on association rule mining," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Kong, Dewei & Lin, Zelong & Li, Wei & He, Wei, 2024. "Development of an improved Bayesian network method for maritime accident safety assessment based on multiscale scenario analysis theory," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    3. Wang, Hong & Chen, Ning & Wu, Bing & Guedes Soares, C., 2024. "Human and organizational factors analysis of collision accidents between merchant ships and fishing vessels based on HFACS-BN model," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    4. Chang, Chia-Hsun & Kontovas, Christos & Yu, Qing & Yang, Zaili, 2021. "Risk assessment of the operations of maritime autonomous surface ships," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    5. Fan, Shiqi & Yang, Zaili, 2024. "Accident data-driven human fatigue analysis in maritime transport using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Yu, Yuerong & Liu, Kezhong & Fu, Shanshan & Chen, Jihong, 2024. "Framework for process risk analysis of maritime accidents based on resilience theory: A case study of grounding accidents in Arctic waters," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    7. Yaling Li & Zhiyou Cheng & Tsz Leung Yip & Xiaobiao Fan & Bing Wu, 2022. "Use of HFACS and Bayesian network for human and organizational factors analysis of ship collision accidents in the Yangtze River," Maritime Policy & Management, Taylor & Francis Journals, vol. 49(8), pages 1169-1183, November.
    8. Munim, Ziaul Haque & Sørli, Michael André & Kim, Hyungju & Alon, Ilan, 2024. "Predicting maritime accident risk using Automated Machine Learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    9. Shu, Yaqing & Han, Bingyu & Song, Lan & Yan, Tao & Gan, Langxiong & Zhu, Yuxin & Zheng, Chunmiao, 2024. "Analyzing the spatio-temporal correlation between tide and shipping behavior at estuarine port for energy-saving purposes," Applied Energy, Elsevier, vol. 367(C).
    10. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Yang, Zaili, 2023. "Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    11. Liu, Jie & He, Zihan & Miao, Yonghao, 2024. "Causality-based adversarial attacks for robust GNN modelling with application in fault detection," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    12. Andrew Rawson & Mario Brito, 2023. "A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis," Transport Reviews, Taylor & Francis Journals, vol. 43(1), pages 108-130, January.
    13. Li, Huanhuan & Ren, Xujie & Yang, Zaili, 2023. "Data-driven Bayesian network for risk analysis of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hörteborn, Axel & Ringsberg, Jonas W. & Lundbäck, Olov & Mao, Wengang, 2025. "Probabilistic analysis of ship-bridge allisions when designing bridges," Reliability Engineering and System Safety, Elsevier, vol. 260(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qiao, Weiliang & Huang, Enze & Zhang, Meng & Ma, Xiaoxue & Liu, Dong, 2025. "Risk influencing factors on the consequence of waterborne transportation accidents in China (2013–2023) based on data-driven machine learning," Reliability Engineering and System Safety, Elsevier, vol. 257(PA).
    2. Cao, Yuhao & Iulia, Manole & Majumdar, Arnab & Feng, Yinwei & Xin, Xuri & Wang, Xinjian & Wang, Huanxin & Yang, Zaili, 2025. "Investigation of the risk influential factors of maritime accidents: A novel topology and robustness analytical framework," Reliability Engineering and System Safety, Elsevier, vol. 254(PB).
    3. Chen, Pengxv & Zhang, Anmin & Zhang, Shenwen & Dong, Taoning & Zeng, Xi & Chen, Shuai & Shi, Peiru & Wong, Yiik Diew & Zhou, Qingji, 2025. "Maritime Near-Miss prediction framework and model interpretation analysis method based on Transformer neural network model with multi-task classification variables," Reliability Engineering and System Safety, Elsevier, vol. 257(PB).
    4. Wang, Yuhong & Li, Pengchang & Hong, Cheng & Yang, Zaili, 2025. "Causation analysis of ship collisions using a TM-FRAM model," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    5. Liu, Guanyi & Liu, Shifeng & Li, Xuewei & Li, Xueyan & Gong, Daqing, 2025. "Multiscenario deduction analysis for railway emergencies using knowledge metatheory and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    6. Guo, Mingyang & Chen, Miao & Yuan, Lihao & Zhang, Zhihui & Lv, Jia & Cai, Zhiyong, 2025. "Investigation of ship collision accident risk factors using BP-DEMATEL method based on HFACS-SCA," Reliability Engineering and System Safety, Elsevier, vol. 257(PB).
    7. Bairami-Khankandi, Shahrokh & Bolbot, Victor & BahooToroody, Ahmad & Goerlandt, Floris, 2025. "A systems-theoretic approach using association rule mining and predictive Bayesian trend analysis to identify patterns in maritime accident causes," Reliability Engineering and System Safety, Elsevier, vol. 258(C).
    8. Hörteborn, Axel & Ringsberg, Jonas W. & Lundbäck, Olov & Mao, Wengang, 2025. "Probabilistic analysis of ship-bridge allisions when designing bridges," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    9. Ding, Song & Hu, Lunhu & Pan, Xing & Zuo, Dujun & Sun, Liuwang, 2025. "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    10. Fan, Hanwen & Jia, Haiying & He, Xuzhuo & Lyu, Jing, 2024. "Navigating uncertainty: A dynamic Bayesian network-based risk assessment framework for maritime trade routes," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    11. Li, Xin & Chen, Chao & Hong, Yi-du & Yang, Fu-qiang, 2023. "Exploring hazardous chemical explosion accidents with association rules and Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    12. Zhou, Kaiwen & Xing, Wenbin & Wang, Jingbo & Li, Huanhuan & Yang, Zaili, 2024. "A data-driven risk model for maritime casualty analysis: A global perspective," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    13. Meng, Huixing & Zhao, Shijun & Song, Wenjuan & Hu, Mengqian, 2025. "Virtual-reality-generated-data-driven Bayesian networks for risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    14. Meng, Huixing & Hu, Mengqian & Kong, Ziyan & Niu, Yiming & Liang, Jiali & Nie, Zhenyu & Xing, Jinduo, 2024. "Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    15. Fu, Shanshan & Tang, Qinya & Zhang, Mingyang & Han, Bing & Wu, Zhongdai & Mao, Wengang, 2025. "A data-driven framework for risk and resilience analysis in maritime transportation systems: A case study of domino effect accidents in arctic waters," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    16. Iaiani, Matteo & Fazari, Giuseppe & Tugnoli, Alessandro & Cozzani, Valerio, 2025. "Identification of reference security scenarios from past event datasets by Bayesian Network analysis," Reliability Engineering and System Safety, Elsevier, vol. 254(PB).
    17. Sun, Xuting & Hu, Yue & Qin, Yichen & Zhang, Yuan, 2024. "Risk assessment of unmanned aerial vehicle accidents based on data-driven Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    18. Li, Duowei & Wong, Yiik Diew & Chen, Tianyi & Wang, Nanxi & Yuen, Kum Fai, 2024. "An ensemble method for investigating maritime casualties resulting in pollution occurrence: Data augmentation and feature analysis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    19. Yu, Yuerong & Liu, Kezhong & Kong, Wei & Xin, Xuri, 2025. "Time-evolving graph-based approach for multi-ship encounter analysis: Insights into ship behavior across different scenario complexity levels," Transportation Research Part A: Policy and Practice, Elsevier, vol. 194(C).
    20. Fan, Shiqi & Shi, Kun & Weng, Jinxian & Yang, Zaili, 2025. "Letting losses be lessons: Human-machine cooperation in maritime transport," Reliability Engineering and System Safety, Elsevier, vol. 253(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:257:y:2025:i:pa:s0951832025000079. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.