This paper is aimed to propose a hybrid accident analysis framework for complex engineering systems based on methods of the systems theoretic accident model and process (STAMP), failure modes and effects analysis (FMEA), and analytic hierarchy process (AHP). Within the scope of the proposed model, initially, an accident is qualitatively analyzed by identifying safety constraints, constructing a hierarchical control process, and specifying links between unsafe activities. As a second step, failure modes, their causes, and their consequences are determined. Lastly, the risk scores of each failure mode are calculated by analysis of obtained expert judgments. In this step, as different from the conventional FMEA, results are calculated based on the weight scores that are found according to the AHP approach of each accident factor. For the demonstration stage, a real case study is performed to present the effectiveness of the strategy. It is observed that the proposed approach allows analyzing the accident more specifically by defining each relationship between factors and root causes corresponding to the accident and prioritizing them based on the weights, which are assigned according to the accident's nature. In addition, it may be adapted for different complex engineering systems as well as it is a suitable and useful model for the accident analysis associated with smart ship concepts.

Traffic accidents constitute a major problem worldwide. One of the principal causes of traffic accidents is adverse driving behavior that is inherently influenced by traffic conditions and infrastructure among other parameters. Probabilistic models for the assessment of road accidents risk usually employs machine learning using historical data of accident records. The main drawback of these approaches is limited coverage of traffic data. This study illustrates a prototype approach that escapes from this problem, and highlights the need to enhance historical accident records with traffic information for improved road safety analysis. Traffic conditions estimation is achieved through Dynamic Traffic Assignment (DTA) simulation that utilizes temporal aspects of a transportation system. Accident risk quantification is achieved through a Bayesian Networks (BNs) model learned from the method’s enriched accidents dataset. The study illustrates the integration of BN with the DTA-based simulator, Visual Interactive Systems for Transport Algorithms (VISTAs), for the assessment of accident risk index (ARI), used to identify accident black spots on road networks.