Considering the challenges currently faced in the civil air traffic management (ATM) domain, this study investigates an aircraft safety situation awareness method based on ADS-B surveillance data. First, a sector safety situation assessment framework is constructed, incorporating six-dimensional indicators such as peak-hour traffic volume and speed standard deviation. The information entropy method is employed to determine objective weights, and an improved fuzzy C-means clustering algorithm is used to classify safety situations into three levels: “good,” “moderate,” and “attention required.” On this basis, multiple prediction models are compared, among which the random forest model achieves the best performance with an accuracy of 90%. Experimental results indicate that, under the experimental conditions of this study, speed standard deviation and approach rate contribute most significantly to safety situation assessment. The proposed method provides air traffic management authorities with an objective and quantifiable technical solution for safety situation awareness.
Liu et al. (Mon,) studied this question.