关键词: 空中交通流量管理, Agent模型, 机场网络, 延误预测, 数据挖掘

Abstract: Accurate and reliable airport network flight delay prediction is an important basis for scientifically understanding the air traffic situation and dynamically and accurately implementing the national airspace system capacity coordination strategy. This paper proposes an Agent airport network delay model, characterizing the emergence of delay characteristics under the interaction between elements and subsystems in the airport network system. Several data mining algorithms were selected to estimate key parameters in the Agent model such as the dynamic capacity of airport nodes, estimated departure time, minimum flight, and turnaround time. The 2015-2017 American national historical flight and weather data for training and learning were used to train these parameter models. To comprehensively evaluate the model performance and generalization ability, three typical days with different delays in the United States in 2018 were selected for testing. The experimental results show that in the network composed of 34 core airports in the United States, the maximum delay error of each node in the 4-hour prediction interval is only 27.9 minutes, and about 80% of the node errors are less than 5 minutes, verifying the accuracy and robustness of the proposed delay prediction model in the space-time range. Comparison with other models further demonstrates the excellent delay prediction performance of our model.

Key words: air traffic flow management, Agent model, airport network, delay prediction, data mining