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In order to be able to accurately assess the Human Error Probability (HEP) of high-speed rail train dispatchers. a HEP quantitative evaluation model based on Improved Weighted Cognitive Reliability and Error Analysis (CREAM) was constructed, which corrected the shortcomings of traditional CREMA. Using bipolar 2-tuples as the Common Performance Condition (CPC) evaluation linguistics, the subjective and objective weights of CPC are calculated through the Analytic Hierarchy Process and the Criteria Importance Though Intercriteria Correlation (CRITIC), and then the combined weighting method is used to further obtain the comprehensive weight of CPC ; At the same time, the idea of group decision making is used to reduce the subjectivity of CPC evaluation, and the weight of experts is calculated by using the adaptive dynamic weight adjustment method of grey correlation degree; on the basis of obtaining CPC and expert weights, the evaluation value of CPC is calculated. The weighted operation then obtains the Context Influence Index (CII) value; finally, the quantitative calculation of HEP is obtained by using the CII value. The model is validated by taking the high-speed train dispatcher's normal and abnormal train reception as examples. The research results show that: under normal conditions, the HEP of high-speed rail train dispatchers is 9.1586×10-5, and under abnormal conditions, the HEP of highspeed rail train dispatchers is 2.1189×10-3. Under abnormal circumstances, the human error probability of high-speed train dispatchers is more than 20 times that under normal circumstances. At the same time, compared with other methods, the model has certain advantages in terms of weight sensitivity, data utilization sufficiency, and model solution domain. The improved CREAM model can better reflect the impact of different CPCs on high-speed rail train dispatchers, and can provide a calculation method for the HEP quantification of high-speed rail train dispatchers.
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