IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v230y2023ics095183202200549x.html

Determining the critical risk factors for predicting the severity of ship collision accidents using a data-driven approach

Author

Listed:
  • Lan, He
  • Ma, Xiaoxue
  • Qiao, Weiliang
  • Deng, Wanyi

Abstract

Ship collision accidents often result in serious casualties and property losses. Predicting the severity of ship collisions is beneficial to improve maritime transport safety. Therefore, this study proposes a data-driven approach integrating association rule mining (ARM), complex network (CN), and random forest (RF) to explore the correlation among risk factors and determine the critical risk factors for predicting the severity of ship collision accidents. Specifically, ARM is integrated with CN to develop the risk interaction network of ship collisions and to identify the criticality of risk factors. Then, RF is employed to predict the severity of ship collisions, and determine the risk factors that have a critical effect on severity prediction. The results show that poor team communication is the most critical risk factor for predicting the severity of ship collisions. Moreover, the criticality of risk factors is different in the risk networks and prediction model. Results from this study would help relevant stakeholders to assess current risks and tailor safety strategies to reduce the severity of ship collisions.

Suggested Citation

  • Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Deng, Wanyi, 2023. "Determining the critical risk factors for predicting the severity of ship collision accidents using a data-driven approach," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s095183202200549x
    DOI: 10.1016/j.ress.2022.108934
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183202200549X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.108934?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Panpan & Wang, Tiandong & Yan, Jun, 2022. "PageRank centrality and algorithms for weighted, directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    2. Qiao, Wanguan & Liu, Quanlong & Li, Xinchun & Luo, Xixi & Wan, YuLong, 2018. "Using data mining techniques to analyze the influencing factor of unsafe behaviors in Chinese underground coal mines," Resources Policy, Elsevier, vol. 59(C), pages 210-216.
    3. Çakır, Erkan & Fışkın, Remzi & Sevgili, Coşkan, 2021. "Investigation of tugboat accidents severity: An application of association rule mining algorithms," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    4. Wang, Huanxin & Liu, Zhengjiang & Wang, Xinjian & Graham, Tony & Wang, Jin, 2021. "An analysis of factors affecting the severity of marine accidents," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    5. Galateia Terti & Isabelle Ruin & Jonathan J. Gourley & Pierre Kirstetter & Zachary Flamig & Juliette Blanchet & Ami Arthur & Sandrine Anquetin, 2019. "Toward Probabilistic Prediction of Flash Flood Human Impacts," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 140-161, January.
    6. Lam, C.Y. & Tai, K., 2020. "Network topological approach to modeling accident causations and characteristics: Analysis of railway incidents in Japan," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    7. Zhaochen Wang & Jingbo Yin, 2020. "Risk assessment of inland waterborne transportation using data mining," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 633-648, July.
    8. Ma, Xiaoxue & Deng, Wanyi & Qiao, Weiliang & Lan, He, 2022. "A methodology to quantify the risk propagation of hazardous events for ship grounding accidents based on directed CN," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Jia, Xiaohui & Zhang, Donghui, 2021. "Prediction of maritime logistics service risks applying soft set based association rule: An early warning model," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    10. Sotiralis, P. & Ventikos, N.P. & Hamann, R. & Golyshev, P. & Teixeira, A.P., 2016. "Incorporation of human factors into ship collision risk models focusing on human centred design aspects," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 210-227.
    11. Fu, Lipeng & Wang, Xueqing & Zhao, Heng & Li, Mengnan, 2022. "Interactions among safety risks in metro deep foundation pit projects: An association rule mining-based modeling framework," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    12. Zhou, Ying & Li, Chenshuang & Ding, Lieyun & Sekula, Przemyslaw & Love, Peter E.D. & Zhou, Cheng, 2019. "Combining association rules mining with complex networks to monitor coupled risks," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 194-208.
    13. Huang, Wencheng & Kou, Xingyi & Zhang, Yue & Mi, Rongwei & Yin, Dezhi & Xiao, Wei & Liu, Zhanru, 2021. "Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    14. Bye, Rolf J. & Aalberg, Asbjørn L., 2018. "Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 174-186.
    15. Zhou, Xiaoyi & Lu, Pan & Zheng, Zijian & Tolliver, Denver & Keramati, Amin, 2020. "Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    16. Weiliang Qiao & Yu Liu & Xiaoxue Ma & Yang Liu, 2020. "Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network," Risk Analysis, John Wiley & Sons, vol. 40(5), pages 957-980, May.
    17. Kaptan, Mehmet & Uğurlu, Özkan & Wang, Jin, 2021. "The effect of nonconformities encountered in the use of technology on the occurrence of collision, contact and grounding accidents," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    18. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    19. Liu, Zengkai & Ma, Qiang & Cai, Baoping & Shi, Xuewei & Zheng, Chao & Liu, Yonghong, 2022. "Risk coupling analysis of subsea blowout accidents based on dynamic Bayesian network and NK model," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    20. 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).
    21. Matthew R. Hallowell & Dillon Alexander & John A. Gambatese, 2017. "Energy-based safety risk assessment: does magnitude and intensity of energy predict injury severity?," Construction Management and Economics, Taylor & Francis Journals, vol. 35(1-2), pages 64-77, February.
    22. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao, 2022. "On the causation of seafarers’ unsafe acts using grounded theory and association rule," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    23. Guo, Shengyu & Zhou, Xinyu & Tang, Bing & Gong, Peisong, 2020. "Exploring the behavioral risk chains of accidents using complex network theory in the construction industry," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    Full references (including those not matched with items on IDEAS)

    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. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao, 2022. "On the causation of seafarers’ unsafe acts using grounded theory and association rule," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. 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).
    3. 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).
    4. Zhang, Hengqi & Geng, Hua, 2023. "A methodology to identify and assess high-risk causes for electrical personal accidents based on directed weighted CN," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    5. 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).
    6. Xiaoxue Ma & He Lan & Weiliang Qiao & Bing Han & Heilong He, 2024. "On the causation correlation of maritime accidents based on data mining techniques," Journal of Risk and Reliability, , vol. 238(5), pages 905-919, October.
    7. 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).
    8. Ma, Xiaoxue & Deng, Wanyi & Qiao, Weiliang & Lan, He, 2022. "A methodology to quantify the risk propagation of hazardous events for ship grounding accidents based on directed CN," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Yue Ma & Qing Liu & Liu Yang, 2025. "A seafarers’ cognitive error mining model based on IDA and TRACEr and mitigation measures for ship collision accidents," Journal of Risk and Reliability, , vol. 239(4), pages 720-735, August.
    10. Han, Yuanwen & Shen, Jiang & Zhu, Xuwei & Bao, Xueying, 2025. "Two-stage propagation analysis of safety risks in complex underground engineering: An integrated modeling framework," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    11. Dugan, Spencer August & Utne, Ingrid Bouwer, 2025. "Improved identification of maritime risk-influencing factors using AIS data in regression analysis," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
    12. Wang, JinHui & Zhou, HongRui & Zhao, BoYing & Wang, LiKun & Zhuang, Lei, 2026. "Spatiotemporal patterns of global cargo ship accidents: An enhanced association rule mining approach," Reliability Engineering and System Safety, Elsevier, vol. 268(C).
    13. Wang, Wenhao & Wang, Yanhui & Wang, Guangxing & Li, Man & Jia, Limin, 2023. "Identification of the critical accident causative factors in the urban rail transit system by complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    14. Ren, Qiaoqiao & Hu, Chengxuan & Xu, Min & Song, Jiatong, 2026. "Association rule mining of damage severities in autonomous vehicle rear-end crashes with supplementary built environment data," Reliability Engineering and System Safety, Elsevier, vol. 265(PA).
    15. Fu, Shanshan & Yu, Yuerong & Chen, Jihong & Xi, Yongtao & Zhang, Mingyang, 2022. "A framework for quantitative analysis of the causation of grounding accidents in arctic shipping," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    16. Fu, Shanshan & Cui, Mengfei & Wu, Ningji & Zhang, Mingyang & Lang, Xiao & Mao, Wengang, 2026. "Evolution trends and influencing factors analysis for the severity and pollution of maritime accidents in Arctic waters from multi-source data," Reliability Engineering and System Safety, Elsevier, vol. 266(PA).
    17. 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).
    18. Feng, Yinwei & Wang, Xinjian & Chen, Qilei & Yang, Zaili & Wang, Jin & Li, Huanhuan & Xia, Guoqing & Liu, Zhengjiang, 2024. "Prediction of the severity of marine accidents using improved machine learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).
    19. 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).
    20. Fan, Cunlong & Qiu, Yuhui & Montewka, Jakub & Bolbot, Victor & Hu, Shenping, 2026. "Advanced framework for risk coupling analysis applied to ship groundings in Shenzhen port," Reliability Engineering and System Safety, Elsevier, vol. 268(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:230:y:2023:i:c:s095183202200549x. 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.