Study on air-rail intermodal ticketing optimization based on K-means clustering A case study of Qingdao Jiaodong International Airport

To improve the intermodal service at Qingdao Jiaodong Airport, addressing operational challenges such as fuzzy passenger demand layering and insufficient cross-modal coordination, and to solve the core issues of supply-demand mismatches and a single pricing mechanism in the air-rail intermodal ticketing system, this study proposes a personalized ticketing optimization strategy based on user profiling. First, through extensive survey data, the study analyzes the personal attributes and travel characteristics of the surveyed passengers. Then, using the K-means clustering algorithm, the study clusters passengers' multidimensional features and determines the optimal number of clusters through the elbow method and silhouette coefficient method. This leads to the establishment of differentiated user labels: economy-class passengers, business-class passengers, and leisure-class passengers. The market segmentation research on passenger groups shows that these three distinct groups perceive the bottlenecks of intermodal services differently, especially exhibiting significant layering features in the key dimensions of time sensitivity and price sensitivity. The results provide a comparative scheme for improving the air-rail intermodal ticketing service at Qingdao Jiaodong International Airport, offering differentiated service strategies for each passenger group. Through responsive demand and resource optimization, this study has significant practical implications for enhancing passenger experience and strengthening the market competitiveness of the service.