Accident analysis model based on Bayesian Network and Evidential Reasoning approach

In this paper, an accident analysis model is proposed to develop the cost-efficient safety measures for preventing accidents. The model comprises two parts. In the first part, a quantitative accident analysis model is built by integrating Human Factors Analysis and Classification System (HFACS) with Bayesian Network (BN), which can be utilized to present the corresponding prevention measures. In the second part, the proposed prevention measures are ranked in a cost-effectiveness manner through Best-Fit method and Evidential Reasoning (ER) approach. A case study of vessel collision is analyzed as an illustration. The case study shows that the proposed model can be used to seek out accident causes and rank the derived safety measures from a cost-effectiveness perspective. The proposed model can provide accident investigators with a tool to generate cost-efficient safety intervention strategies.

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► Proposes an accident analysis model to develop cost-efficient safety measures.
► Integrate Human Factors Analysis and Classification System with Bayesian Network.
► BN is developed up to risk ranking and measures identification and effectiveness.
► Best-Fit and Evidential Reasoning are applied to rank the cost-effectiveness of measures.
► AHP and decomposition method are integrated to estimate conditional probabilities.