An Integrated FMEA–HFACS–Bayesian Framework for Railway Risk Assessment: Specification, Survey-Informed Parameterisation and Demonstration

Background: Integrating human factors into quantitative railway risk assessment remains methodologically unresolved. Failure Mode and Effects Analysis (FMEA) records human contributions as a single occurrence rating and cannot represent their organisational antecedents or their interactions. Purpose: This paper is a methodological proposal. It specifies the Integrated Human–Technical Risk Assessment (IHTRA) framework, which combines FMEA, the Human Factors Analysis and Classification System (HFACS) and Bayesian network modelling within the EN 50126 RAMS lifecycle, and demonstrates what such a specification makes analytically possible. It does not claim to validate the framework empirically. Methods: The Bayesian layer is specified in full as a ten-node network with all conditional probability tables reported. Of its twenty-two endogenous parameters, eight rest on evidence: four are derived from a survey of 89 Hungarian train drivers and four from the published fatigue literature. The remaining fourteen are declared structured assumptions awaiting expert elicitation. A conventional FMEA and the specified framework were both applied to national investigation report 2023-1152-5 (Sáp collision, 2023). Results: Human factors awareness yielded the highest domain mean (M = 4.23, SD = 1.18), with near-unanimous recognition of fatigue (M = 4.90) and workload (M = 4.87). Organisational consideration of human factors scored lowest (M = 2.66). No significant experience-group differences were observed (p > 0.05). The case analysis identified two HFACS levels as confirmed by the investigation findings and two further levels as plausible under the model interpretation. Inference over the specified network gives an illustrative, but not yet empirically calibrated, increase from p = 0.00038 to p = 0.00059 for the unsafe act and from p = 0.00011 to p = 0.00047 for the collision outcome. Conclusions: A specified but uncalibrated framework is a methodological contribution rather than an empirical one and is presented as such. This paper states precisely which parameters would have to be measured, and by what protocol, for the framework to become operational.