基于异构Agent的航空交通网络供需态势统一建模

doi:10.7527/S1000-6893.2026.33122

Abstract

Abstract:

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.

Key words: air traffic management, supply-demand matching, delay prediction, Agent-based modeling, fluid queuing theory

CLC Number:

V355

Shuce WANG, Minghua HU, Lei YANG, Zhening CHANG, Chunzheng WANG. Unified modeling of supply-demand situations in air traffic network based on heterogeneous Agent[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(15): 333122.

Figures/Tables 16

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航班状态	特征描述
初始态	航班Agent未进入任何状态
周转态	航班Agent经历周转（地面保障时间内），或未到达计划起飞时间的状态，确定目标撤轮挡时刻
预备态	航班Agent已完成周转或超过计划起飞时间，由于本场容量限制原因未能推出，无本场容量限制时准许起飞
飞行态	航班Agent离开机场，正在经历扇区飞行，考虑扇区容量，产生扇区排队延误
到达态	航班Agent经历所有的飞行，所有目标执行完毕

序号	机场ICAO代码	发生时间	容量变化/（架次·h^-1）
1	ZSPD	08：00—19：00	-5
2	ZSPD	21：00—06：00（+1）	-14
3	ZSSS	08：00—11：00	-3
4	ZSNJ	08：00—12：00	-7
5	ZSOF	08：00—14：00	-4

算例	平均延误时间/min	MAE/min	RMSE/min	计算耗时/min
实际运行	9.67
AND模型	5.39	14.37	151.22	4.13
固定容量	8.99	7.13	50.10	4.34
场景容量	9.94	4.58	15.06	10.46

机场ICAO代码	实际平均延误时间/min	AND模型平均延误时间/min	固定容量平均延误时间/min	场景容量平均延误时间/min
ZBAA	21.07	13.06	19.48	22.05
ZBAD	9.52	6.52	8.03	9.90
ZGGG	18.24	12.69	19.18	18.39
ZGSZ	30.54	15.11	26.22	32.47
ZLXY	15.43	13.27	15.92	15.35
ZPPP	10.15	6.29	12.47	11.39
ZSHC	19.32	13.71	17.00	20.12
ZSPD	24.20	14.63	21.52	24.90
ZUCK	12.28	8.54	10.53	11.12
ZUTF	11.08	9.05	10.40	10.67

测试日	预测类型	平均飞行时间±标准差/min	平均延误时间±标准差/min	计算耗时/s
高流量日	实际运行	135.12±70.41	17.30±55.47
	Agent预测	137.14±67.91	16.37±54.25	1.89
	AND预测		12.37±61.25	1.61
低流量日	实际运行	115.05±38.51	5.31±37.03
	Agent预测	116.64±39.22	5.69±47.65	1.70
	AND预测		3.37±64.25	1.61

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

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References 29

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机场	航班数量/架次	实际平均延误时间±标准差/min	异构Agent模型预测平均延误时间±标准差/min	AND模型预测平均延误时间±标准差/min
ZSPD	1 203	14.55±43.68	14.83±47.65	9.19±37.51
ZSSS	633	4.11±19.47	3.97±19.94	3.04±11.82
ZSNJ	510	6.08±30.07	6.36±29.05	5.11±21.65
ZSOF	217	71.40±104.18	74.55±97.23	27.61±32.51

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