Causation analysis of marine traffic accidents using Deep Learning approaches: A case study from China’s coasts

Abstract

In response to the increasing frequency of maritime traffic accidents along China’s coast, this study develops an accident-cause analysis framework that integrates an optimized Bidirectional Encoder Representations from Transformers (BERT) with a Bidirectional Long Short-Term Memory network (BiLSTM), combined with the Apriori association rule algorithm. Systematic performance comparisons demonstrate that the BERT + BiLSTM architecture achieves superior unstructured-text-processing capability, attaining 89.8% accuracy in accident-cause classification. The hybrid framework enables comprehensive investigation of complex interactions among human factors, vessel characteristics, environmental conditions, and management practices through multidimensional analysis of accident reports. Our findings identify improper operations, fatigue-related issues, illegal modifications, and inadequate management practices as primary high-risk factors while revealing that multi-factor interaction patterns significantly influence accident severity. Compared with traditional single-factor analysis methods, the proposed framework shows marked improvements in Natural Language Processing (NLP) efficiency, classification precision, and systematic interpretation of cross-factor correlations. This integrated approach provides maritime authorities with scientific evidence to develop targeted accident prevention strategies and optimize safety management systems, thereby enhancing maritime safety governance along China’s coastline.

Postprint (published version)