Research on automatic hazard identification and risk propagation in TACS operational scenario based on STAMP and complex network

Abstract The Train Autonomous Control System (TACS) represents the future direction of next-generation urban rail transit development. To ensure its high safety, a comprehensive hazard analysis is required. While the Systems-Theoretic Accident Model and Processes (STAMP) theory demonstrates strong applicability in hazard analysis of complex technical systems, it faces challenges, including extensive analytical workload, low efficiency, and difficulties in quantifying coupling relationships between hazardous factors. This paper extends the System-Theoretic Process Analysis (STPA) method based on STAMP theory, proposing an automatic hazard identification algorithm. Building upon the automatic generation of unsafe control actions and causal factors, we establish a capacity-load risk propagation model among hazardous factors using complex network theory. By integrating improved network topology metrics to define model parameters and designing quantitative representations of safety constraints, we investigate risk propagation processes under different protection capabilities. Experimental validation through TACS train turn-back scenarios demonstrates that our method effectively identifies potential hazards with high efficiency and comprehensiveness. The results indicate that enhancing node safety constraints in the proposed risk propagation model successfully contain risk propagation scope and frequency for most hazardous nodes. The residual risk-propagating nodes are systematically identified as critical high-risk targets, providing scientifically grounded priorities for systemic risk control strategies.