Traffic flow modeling and optimization control in the approach airspace

Yunxiang Han; Xiaoqiong Huang

doi:10.3934/jimo.2022109

Article Contents

2023, Volume 19, Issue 5: 3784-3793. Doi: 10.3934/jimo.2022109

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Traffic flow modeling and optimization control in the approach airspace

Yunxiang Han^1,2, and
Xiaoqiong Huang^3, ,

1.
CAAC Key Laboratory of Civil Aviation Wide Surveillance and Safety Operation, Management & Control Technology, Civil Aviation University of China
2.
State Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, China
3.
School of Economics and Management, Southwest Jiaotong University, China

^*Corresponding author: Xiaoqiong Huang
^*Corresponding author: Xiaoqiong Huang

Received: September 2021

Revised: May 2022

Early access: July 2022

Published: May 2023

The first author is supported by "Civil Aviation University of China Open Fund of CAAC Key Laboratory of Civil Aviation Wide Surveillance Safety Operation Management & Control Technology (NO:202103)", "Full-time postdoctoral R&D fund (NO:2021SCU12048)"and"Sichuan Science and Technology Program (NO:2022YFG0180)".

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Abstract

Air traffic control behaviors in civil aviation approach airspace include arrival time control, aircraft sequencing and aircraft diversion. In the airspace with heavy air traffic flow, air traffic controllers need to make the best decision for multiple flights in a given space and time range. The topic of this paper is the optimization in civil aviation approach airspace. In this paper, the flight operation involves multiple different resource, and the flight path selection is reset based on the given flight constraints to optimize the objective function. This paper presents an optimal decision model for controlling the flight flow in the approach airspace. The performance of the model is evaluated in different simulation scenarios.

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Mathematics Subject Classification: Primary: 90B06; Secondary: 90B20.

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Figure 1. Simplified configuration of the approach airspace

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Figure 2. The altitude profiles and speed profiles of departures

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Table 1. Assessment of various configurations for case 1

Approach	Prediction horizon/min	I-1		I-2		I-3
		S-1	S-2	S-1	S-2	S-1	S-2
M1	/	1.35	1.33	1.41	1.38	1.37	1.35
M2	2	1.23	1.21	1.30	1.29	1.35	1.33
	4	1.19	1.18	1.28	1.26	1.32	1.29
	6	1.18	1.16	1.27	1.24	1.31	1.28
	8	1.15	1.13	1.26	1.23	1.28	1.25
	10	1.14	1.12	1.24	1.20	1.26	1.23
M3	2	1.08	1.07	1.18	1.17	1.24	1.20
	4	1.06	1.05	1.16	1.14	1.22	1.19
	6	1.05	1.04	1.10	1.09	1.20	1.18
	8	1.03	1.02	1.08	1.07	1.17	1.15
	10	1.02	1.00	1.05	1.00	1.15	1.00

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Table 2. Assessment of various configurations for case 2

Approach	Prediction horizon/min	Total deviations		Total aircraft number
		S-1	S-2	S-1	S-2
M1	/	1.36	1.34	1.47	1.46
M2	2	1.34	1.30	1.44	1.43
	4	1.31	1.29	1.43	1.42
	6	1.28	1.27	1.41	1.40
	8	1.25	1.24	1.40	1.39
	10	1.24	1.22	1.36	1.33
M3	2	1.21	1.20	1.33	1.31
	4	1.19	1.17	1.30	1.28
	6	1.16	1.15	1.27	1.25
	8	1.14	1.13	1.24	1.22
	10	1.12	1.00	1.21	1.00

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Table 3. Assessment of various configurations for case 3

Approach	Prediction horizon/min	(I-1, I-2)	(I-1, I-3)	(I-2, I-3)
M1	/	(1.33, 1.34)	(1.29, 1.33)	(1.29, 1.31)
M2	2	(1.27, 1.29)	(1.26, 1.31)	(1.26, 1.27)
	4	(1.22, 1.25)	(1.23, 1.28)	(1.23, 1.24)
	6	(1.20, 1.24)	(1.21, 1.25)	(1.21, 1.21)
	8	(1.19, 1.21)	(1.19, 1.23)	(1.18, 1.20)
	10	(1.17, 1.18)	(1.18, 1.20)	(1.16, 1.18)
M3	2	(1.14, 1.16)	(1.16, 1.17)	(1.15, 1.17)
	4	(1.09, 1.13)	(1.13, 1.14)	(1.12, 1.11)
	6	(1.08, 1.11)	(1.10, 1.11)	(1.09, 1.10)
	8	(1.05, 1.07)	(1.07, 1.09)	(1.06, 1.08)
	10	(1.02, 1.03)	(1.04, 1.06)	(1.05, 1.04)

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