Abstract: In the air traffic system, different decision-making stages lack a unified modeling tool for supply–demand situation assessment, making it difficult to support multi-level and multi-stage collaborative decision-making. To address this issue, this study develops a unified modeling framework for air traffic network supply–demand situations based on heterogeneous agents. First, the theoretical analysis demonstrates that the completeness of the airspace network structure has a decisive impact on the accuracy of delay characterization, and clarifies the functional relationship between network node completeness and prediction error. Then, by integrating the agent interaction mechanism with fluid queuing theory, a dynamic multi-element coupling framework covering flights, airports, and airspace is constructed. Three types of heterogeneous agents, flight, airport, and sector, are defined to establish state transition and congestion/delay propagation mechanisms. Based on historical ADS-B trajectory data, sector service times are calibrated, and the main input parameters of the sector fluid queuing system are determined, enabling the mapping and parallel simulation of system operating states across multiple levels. Using data from 250 airports, 287 sectors, and seasonal flight schedules across China, the model is validated in three representative scenarios: flight schedule configuration, next-day flight planning, and sudden capacity degradation. The results show that the heterogeneous-agent model achieves higher prediction accuracy than existing methods in all scenarios, realizing an integrated “flight-airport-airspace” supply–demand analysis across strategic, pre-tactical, and tactical decision-making stages, and providing a reliable, accurate, and efficient decision-support tool for air traffic system planning, evaluation, and operational management.

Key words: air traffic management, supply-demand matching, multi layer network, delay prediction, agent based modeling, fluid queuing theory