Jul 2026· The Scientist· Vol 8, pp. 158· 0 citations· 54 references
TL;DR
A data-driven decision-support framework based on a Multi-Input Multi-Output Artificial Neural Network (MIMO-ANN) for the simultaneous prediction of multiple maritime accident consequences, which shows that accident type, zone, flag, natural light, environment, and visibility are key drivers of predicted consequences, whereas vessel-specific parameters have a secondary, context-dependent influence.
Abstract
Maritime accident analysis increasingly uses machine learning to support safety management, but many existing studies focus on single-output prediction, such as accident-occurrence probability, severity class, near-miss frequency, or one specific consequence. This study proposes a data-driven decision-support framework based on a Multi-Input Multi-Output Artificial Neural Network (MIMO-ANN) for the simultaneous prediction of multiple maritime accident consequences. A dataset of 582 recorded accident cases is constructed by integrating SafePASS project records with consequence, severity, and structural-damage information from the literature. The dataset includes 15 input variables covering ship characteristics, operational context, environmental conditions, accident type, and geographical zone and 15 consequence outputs covering structural damage, casualties, emergency-response indicators, total loss, and secondary consequence/escalation mechanisms. The ANN is trained using the Scaled Conjugate Gradient (SCG) algorithm and evaluated under different network configurations and data-partitioning strategies. The best-performing model uses 30 hidden neurons with a 60/20/20 split, achieving a correlation coefficient (R) equal to 0.9249 and a mean squared error (MSE) equal to 0.0240 for testing, and a R equal to 0.9278 and a MSE equal to 0.0231 for validation. Ten-fold cross-validation further confirms internal predictive stability, with mean testing R equal to 0.8803 ± 0.0827 and MSE equal to 0.0445 ± 0.0478. Permutation-based sensitivity analysis shows that accident type, zone, flag, natural light, environment, and visibility are key drivers of predicted consequences, whereas vessel-specific parameters have a secondary, context-dependent influence. The framework should be interpreted as predicting the relative likelihood, severity, or magnitude of accident consequences in recorded or scenario-defined accident cases, not the probability of accident occurrence. Future work should address dataset imbalance, include near-miss and nonserious records, incorporate richer AIS and metocean data, integrate exposure data, and validate the framework using independent accident datasets.
Traffic accident severity prediction is important for improving traffic safety management and supporting risk assessment and prevention. However, current studies have several limitations with respect to the systematic approach for comparing models and the comprehensiveness of evaluation metrics. This paper aims to systematically assess the overall performance of various machine learning models on a unified experimental framework to predict the severity of accidents in a binary classification task. This study randomly sampled 500,000 records from the US Accidents dataset and used them as the experimental sample. Following data cleaning, missing value handling, feature engineering, and categorical variable encoding, a comparison of four representative models was performed: Logistic Regression (LR), Random Forest (RF), XGBoost and Multi-Layer Perceptron (MLP). With the imbalanced nature of the data, this paper evaluates model performance using accuracy, AUC, ROC curves, and confusion matrices to assess the overall classification performance of different models and their ability to identify the severe accident class. The results show that the tree-based ensemble models Random Forest and XGBoost outperform Logistic Regression and MLP in terms of overall predictive performance, with XGBoost exhibiting better overall performance. Variables that contribute significantly to the model’s predictions include traffic control facilities, time factors, spatial location, and accident-affected distance. The results indicate that machine learning techniques could be useful in traffic accident severity prediction and could be a reference for traffic safety risk assessment. Meanwhile, class imbalance, feature interpretability and model generalizability are challenges that need further investigation and resolution.
Zi-Yu Cao· Frontiers in Computing and I...· 0 citations
In maritime accident prevention, it is important to identify not only high-risk sea areas but also which accident-types are most likely to occur there. This study combines survey responses from 826 Korea Coast Guard practitioners with 3856 maritime accidents mapped onto an H3 grid over Korean territorial waters during 2021–2023, and proposes a practitioner-informed framework for predicting accident-type-specific risk. The survey showed limited use of quantitative, standardized accident risk criteria but high demand for AI-based prediction and area-level risk analysis. Practitioners’ perceived accident frequency differed substantially from the empirical accident distribution, whereas their prevention priorities aligned more closely with the actual pattern. Accordingly, this study treats the accident-type taxonomy not as a fixed prediction target but as a design variable of the label space for decision support. A two-stage framework first estimates accident occurrence at the H3 grid-time level and then classifies the accident-type conditional on occurrence. Comparing survey-aligned, data-aligned, union, sufficient-sample, and full administrative (7-class) framings under a common training protocol shows that accident-type organization creates trade-offs among field interpretability, coverage, class granularity, and predictive stability. The study thus reframes maritime accident prediction as an accident-type-specific decision-support problem-linking practitioner perception with empirical evidence.
Dayoung Kim, Won Choi, Seung Sim et al.· Journal of Marine Science an...· 0 citations
Road traffic accidents continue to pose a significant challenge to public safety, resulting in substantial human suffering, economic losses, and increasing pressure on transportation systems. This study proposes a data-driven intelligent transportation framework that integrates machine learning and spatial network analysis to support accident severity prediction, risk-aware route recommendation, and emergency response. A comprehensive dataset comprising traffic conditions, weather information, temporal attributes, roadway characteristics, vehicle information, and driver-related factors was analysed to identify the key determinants of accident severity. Multiple machine-learning models were evaluated, and the Multi-Layer Perceptron (MLP) classifier achieved the highest predictive performance, attaining an overall accuracy of 91.2%. To transform predictive outcomes into practical safety interventions, the proposed framework combines accident severity prediction with GPS-enabled spatial network analysis to identify high-risk road segments and recommend safer alternative routes. In addition, an automated SMS notification mechanism is incorporated to provide location-aware emergency alerts when high-risk situations are detected. The integration of predictive analytics, spatial risk assessment, safety-oriented routing, and emergency communication establishes a comprehensive decision-support framework for proactive accident prevention and transportation-safety management. The experimental results demonstrate that the proposed approach can effectively support safer mobility, improved situational awareness, and enhanced emergency response within intelligent transportation environments.
V. Naresh, Ayyappa Dullam· European Transport Research...· 0 citations
This study presents a novel hybrid machine learning approach for analyzing human error factors in maritime accidents using HELCOM accident data. We developed an advanced classification model that combines Random Forest, Gradient Boosting, XGBoost, LightGBM, and neural networks through a soft voting ensemble mechanism. Our methodology includes enhanced data preprocessing techniques, sophisticated feature engineering, and class imbalance correction through SMOTE resampling. The visualizations produced meet publication-quality standards with optimized color schemes, typography, and statistical representations suitable for high-impact journals. The hybrid model achieved 84% accuracy with macro-averaged F1-score of 0.58 across eight accident classes, identifying key human factors contributing to maritime incidents. Our findings indicate that specific human element factors have significant correlations with particular accident types, offering valuable insights for maritime safety policy development and accident prevention strategies. This research contributes to the growing field of data-driven maritime risk assessment by providing a robust methodological framework for human error analysis in the maritime domain.
Prabhat Nigam, Neeraj Anand, Manan Bhasin et al.· International journal of com...· 0 citations
This study develops a data-driven severity-classification approach for maritime construction, relating equipment activity from incident narratives to localized hydro-meteorological conditions to complement static safety systems.
The research analyzed 1,139 maritime construction incidents from OSHA databases (2015–2025). A rule-based Natural Language Processing module classified unstructured narratives into 15 distinct equipment categories. Additionally, an interface to a historical weather API reconstructed micro-climate conditions at the incident locations. Sixteen machine-learning algorithms were comprehensively compared for injury severity classification using chronological hold-outs, stratified cross-validation and feature-ablation.
A Logistic Regression classifier provided a strong balance of discrimination and interpretability, achieving a cross-validated AUC of 89.2% and a hold-out AUC of 95.6% for post-incident classification based on narratives, while a pre-incident configuration using only prior operational factors achieved an AUC of 68.7%, with an F1-score of 95.4% and a Brier score of 0.062. The predictive signal originates primarily in the incident narrative; weather and employer history contributed modestly. A sensitivity analysis confirmed performance was robust to employer-history exclusion, and a localized wind-speed association near 30 km/h was cautiously identified.
The approach offers a conceptual triage aid for site superintendents. If integrated into Construction Safety Management Systems, this logic could prioritize hazard reviews and inform daily planning, pending operational validation.
The study integrates unstructured narratives with quantitative geospatial weather metrics in maritime construction. It reports a highly transparent classifier as an alternative to opaque models, offering an applied integration of established methods for safety-critical risk analysis.
Amr A. Mohy· Smart and Sustainable Built...· 0 citations
Road traffic accident consequences are multidimensional, involving fatalities, injuries, and property loss. Existing studies have mainly focused on single outcomes, limiting the understanding of heterogeneous mechanisms across different consequence dimensions. Based on road accident data from Yancheng City in 2022, this study develops an integrated framework combining multi-output prediction, NSGA-II multi-objective optimization, SHAP-based interpretation, LOWESS nonlinear analysis, and DirectLiNGAM causal inference. The results show that the optimized Voting ensemble achieved competitive and comparatively balanced performance across the three accident-consequence dimensions. Road category, traffic control type, junction/road-segment type, and crash-cause category are identified as key influencing factors, with differentiated effects across accident consequences. POI variables exhibit nonlinear and threshold effects, while causal analysis further indicates that road infrastructure and traffic control conditions are positioned upstream in the formation mechanism of accident consequences. This study provides evidence for multidimensional accident-consequence category prediction and differentiated traffic safety management, rather than traditional continuous regression-based modeling.
Yanni Ju, Wanqiu Li, Di Tang et al.· Applied Sciences· 0 citations
