A new approach to identify critical causal factors and evaluate intervention strategies for mitigating major railway occurrences in Taiwan

Following two consecutive catastrophic railway accidents in Taiwan, public safety concern has been raised in railway transportation services. To improve train operation safety, this study integrates the Human Factors Analysis and Classification System (HFACS), Fuzzy Logic Modeling (FLM) method, and Human Factors Intervention Matrix (HFIX) to develop a safety assessment framework. Twenty eight major railway occurrence investigation reports published by the Taiwan Transportation Safety Board are collected for data extraction. Using the HFACS, causal factors causing major railway occurrences are first classified, followed by critical causal factors identification through FLM method. The HFIX is applied to categorized safety recommendations which were issued based on the identified causal factors of occurrence investigations and pair the results with critical causal factors for accident rate evaluations and effectiveness assessment. The evaluations reveal that the statistical accident rate in 2023 was higher than the predicted accident rate. The results also reveal that mitigating the frequency of identified causal factors is more efficient for occurrences reduction than through safety recommendations enforcement. Therefore, decision makers can determine the best intervention strategies based on available resources and develop relevant countermeasures for implementation. • A new approach to identify critical causal factors of railway occurrences and determine interventions was proposed. • The identified error patterns cover not only individual's unsafe acts but also supervisory deficiencies. • The evaluation criteria can evaluate the efficiencies of interventions to determine appropriate countermeasures for implementation.