Emergency Takeover Performance Evaluation of Train Operators in Semi-Automated Urban Rail Transit: An Attention-Enhanced MLP Approach

Semi-automated urban rail transit systems still rely on human intervention during safety-critical events, yet emergency takeover performance has received far less attention than in SAE Level-3 road automation. This study focuses on the reaction phase of emergency takeover, defined as the interval from anomaly onset to the train operator’s first control action. We propose a conditional two-stage evaluation framework that jointly assesses event recognition and control execution quality. A simulation-based experiment was conducted to replicate GoA2 operating conditions under controlled emergency scenarios. Three indicators were extracted: (i) event recognition accuracy derived from eye-tracking and retrospective recall, (ii) takeover reaction time, and (iii) initial action accuracy reflecting compliance with operational speed or braking limits. An attention-enhanced multilayer perceptron (MLP) was developed to dynamically weight input features and improve interpretability. The proposed model achieved stable subject-wise performance, with an average accuracy of 0.86 and a macro F1-score of 0.857. These results support the feasibility of interpretable learning-based evaluation for human-in-the-loop safety assessment and provide practical implications for improving operator readiness monitoring and operational safety management in semi-automated metro systems.