Unified modeling of supply-demand situations in air traffic network based on heterogeneous Agent

In the air traffic system, constructing a unified supply-demand situations modeling framework for different decision-making stages is critical for efficient multi-level and multi-stage collaborative decision-making. Accordingly, a heterogeneous-Agent-based unified deduction model of supply-demand situations in air traffic network is developed, referred to as the heterogeneous Agent model. 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 unified 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 Automatic Dependent Surveillance-Broadcast (ADS-B) trajectory data, sector service time is calibrated, and the main input parameters of the sector fluid queuing system are determined, enabling the cross-level mapping and parallel deduction of system operating states across multiple levels. Finally, using China-wide flight operation data at the flight-season scale covering 250 airports and 287 sectors as the sample, the model is validated in three scenarios: flight schedule configuration, next-day flight planning, and sudden capacity degradation. The results show that the heterogeneous Agent model achieves higher delay prediction accuracy than existing methods in all scenarios. Capable of integrated “flight-airport-airspace” supply-demand situations analysis across strategic, pre-tactical, and tactical decision-making stages, and providing a reliable, accurate, and efficient decision-support for planning, evaluation and operational management of air traffic system.