Maritime accident severity modeling with operationally realistic multi-factor data-driven bayesian networks

Khan, RU ORCID: 0000-0002-6887-3487 and Yang, Z ORCID: 0000-0003-1385-493X (2026) Maritime accident severity modeling with operationally realistic multi-factor data-driven bayesian networks. Reliability Engineering and System Safety, 275. ISSN 0951-8320

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Abstract

Maritime accident severity analysis supports safety management and emergency preparedness, yet operational realism remains limited in many quantitative models. Common limitations include coarse geographic representation, reduced-granularity met-ocean descriptors, and the omission of Port State Control (PSC) compliance signals. These constraints can mask actionable thresholds and hinder the identification of interpretable, decision-relevant severity pathways. To address this, the study develops Bayesian Network (BN) models to forecast accident severity using a globally distributed, multi-year (2015–2024) dataset of 1050 reports, with granular descriptors of route/region, operating locale, weather, sea-state, visibility, ship profile, human error, and accident type. It assembles a harmonized severity dataset unifying investigation narratives, PSC outcomes, ship registries, and met-ocean validation within a single standardized schema; embeds two-layer geography, multi-state environmental conditions, and compliance signals into one BN severity framework; and benchmarks three BN families, Tree-Augmented Naïve (TAN), Augmented Naïve Bayes (ANB), and a literature/expert topology, on the same dataset. Beyond structure comparison, a unified inference workflow combines Strength-of-Influence, state-wise tornado decomposition, True Risk Influence (TRI), and sequential and simultaneous scenario analysis. All three models were estimated via Expectation–Maximization (EM) and evaluated using stratified 10-fold ROC/AUC. The literature/expert BN achieved the strongest cross-validated discrimination across severity states and was selected for analysis, indicating that structure should be selected empirically rather than assumed a priori. Diagnostic, scenario, and sensitivity analysis show that severity escalates when open-sea operations on dense traffic corridors coincide with adverse environment (storm, poor visibility, rough sea, high winds), human error, and loss-of-control typologies (grounding/capsizing); severity attenuates under benign conditions and contact-type events. Strength-of-Influence results highlight the central roles of route/region→traffic density and weather/visibility→severity pathways. A targeted TRI assessment isolates Accident Type as the dominant lever shifting probability mass into the high-severity state. The findings enable consequence-aware routing and vessel traffic prioritization, type-conditioned emergency preparedness, and human-factor/equipment readiness.

Item Type:	Article
Uncontrolled Keywords:	Maritime safety; Accident severity; Data-driven BN; Compliance signal; Operational locale; Accident geography; 3505 Human Resources and Industrial Relations; 35 Commerce, Management, Tourism and Services; 01 Mathematical Sciences; 09 Engineering; 15 Commerce, Management, Tourism and Services; Strategic, Defence & Security Studies; 35 Commerce, management, tourism and services; 40 Engineering; 49 Mathematical sciences
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions:	Engineering and Built Environment
Publisher:	Elsevier
Date of acceptance:	2 May 2026
Date of first compliant Open Access:	2 September 2026
Date Deposited:	02 Sep 2026 14:09
Last Modified:	02 Sep 2026 14:09
DOI or ID number:	10.1016/j.ress.2026.112823
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29301

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