Analysis of Advance Purchase Behavior of Air–Rail Passengers with Ticket Booking Data

Understanding the advance ticket purchase behavior of air–rail intermodal passengers is essential for travel demand forecasting, schedule coordination optimization, and revenue management. Using actual booking data, this study investigates passengers’ advance purchase time (APT) decisions. A Bayesian network (BN) model integrating expert knowledge and data-driven learning is established, with socioeconomic attributes and ticket characteristics as input variables and APT as the output variable. Based on this BN, group analysis is conducted across four routes of varying distances: Tianjin–Shanghai, Tianjin–Changsha, Tianjin–Guangzhou, and Tianjin–Sanya. The results indicate that socioeconomic and ticket attributes influence advance purchase behavior both directly and indirectly through interactive effects. Inferential analysis reveals that elderly passengers, male travelers, morning departures, and longer-distance trips are associated with earlier ticket purchases. Sensitivity analysis shows that fare, transfer time, and age exert heterogeneous effects across routes. Compared with short- and medium-haul journeys, airfare, rail travel time, and transfer time impose stronger impacts on long-haul intermodal trips. Finally, targeted revenue management strategies are proposed to improve air–rail ticket sales.