Human factors configurations of driver maladaptation in urban rail accidents: An fsQCA-based analysis

This study investigates how different configurations of driver misfit states contribute to emergency incidents in semi-automated metro systems. Building on the Human Factors Analysis and Classification System (HFACS) framework, this study employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to uncover three distinct configurations of maladaptation that result in operational failures. These include safety cognition deficiency, system supervision failure, and attention-skill disintegration. The findings suggest that driver misfit is not the result of a single cause but rather emerges from the interaction of multiple conditions across individual, supervisory, and organizational levels. This research contributes to the theoretical development of HFACS and demonstrates the value of configurational methods in the analysis of urban rail safety. It also offers practical insights for improving training systems, supervisory mechanisms, and operational design to reduce the likelihood of mismatches between driver capabilities and job demands.