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doi: 10.3934/jimo.2022109
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Traffic flow modeling and optimization control in the approach airspace

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

Received  September 2021 Revised  May 2022 Early access July 2022

Fund Project: 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)"

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.

Citation: Yunxiang Han, Xiaoqiong Huang. Traffic flow modeling and optimization control in the approach airspace. Journal of Industrial and Management Optimization, doi: 10.3934/jimo.2022109
References:
[1]

J. A. D. AtkinE. K. BurkeJ. S. Greenwood and D. Reeson, An examination of take-off scheduling constraints at London heathrow airport, Public Transport, 1 (2009), 169-187.  doi: 10.1007/s12469-009-0011-z.

[2]

Y.-H. ChangS. SolakJ.-P. B. Clarke and E. L. Johnson, Models for single-sector stochastic air traffic flow management under reduced airspace capacity, Journal of the Operational Research Society, 67 (2016), 54-67.  doi: 10.1057/jors.2015.53.

[3]

E. P. Gilbo, Optimizing airport capacity utilization in air traffic flow management subject to constraints at arrival and departure fixes, IEEE Transactions on Control Systems Technology, 5 (1997), 490-503.  doi: 10.1109/87.623035.

[4]

A. K. Agogino and K. Tumer, A multiagent approach to managing air traffic flow, Autonomous Agents and Multi-Agent Systems, 24 (2012), 1-25.  doi: 10.1007/s10458-010-9142-5.

[5]

J. A. D. AtkinE. K. Burke and J. S. Greenwood, A comparison of two methods for reducing take-off delay at London Heathrow airport, Journal of Scheduling, 14 (2011), 409-421.  doi: 10.1007/s10951-011-0228-y.

[6]

J. A. D. AtkinE. K. BurkeJ. S. Greenwood and D. Reeson, On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport, Journal of Scheduling, 11 (2008), 323-331.  doi: 10.1007/s10951-008-0065-9.

[7]

J. A. BennellM. Mesgarpour and C. N. Potts, Airport runway scheduling, Annals of Operations Research, 204 (2013), 249-270.  doi: 10.1007/s10479-012-1268-1.

[8]

L. BiancoP. Dell'Olmo and S. Giordani, Scheduling models for air traffic control in terminal areas, Journal of Scheduling, 9 (2006), 223-253.  doi: 10.1007/s10951-006-6779-7.

[9]

M. F. Bongo and L. A. Ocampo, Exploring critical attributes during air traffic congestion with a fuzzy DEMATEL–ANP technique: A case study in Ninoy Aquino International Airport, Journal of Modern Transportation, 26 (2018), 147-161.  doi: 10.1007/s40534-017-0150-x.

[10]

J. Coletsos and C. Ntakolia, Air traffic management and energy efficiency: The free flight concept, Energy Systems, 67 (2017), 709-726.  doi: 10.1007/s12667-015-0165-5.

[11]

C. D'ApiceC. De NicolaR. Manzo and V. Moccia, Optimal scheduling for aircraft departures, Journal of Ambient Intelligence and Humanized Computing, 5 (2014), 799-807.  doi: 10.1007/s12652-014-0223-1.

[12]

P. L. de MatosB. Chen and R. J. Ormerod, Optimisation models for re-routing air traffic flows in Europe, Journal of the Operational Research Society, 52 (2001), 1338-1349.  doi: 10.1057/palgrave.jors.2601224.

[13]

Y. DongZ. LuY. LiuQ. Zhang and D. Wu, China's corridors-in-the-sky design and space-time congestion identification and the influence of air routes' traffic flow, Journal of Geographical Sciences, 29 (2019), 1999-2014.  doi: 10.1007/s11442-019-1701-3.

[14]

EUROCONTROL, Point Merge implementation-simplifying and enhancing arrival operations with closed loop sequencing, Report, (2021), available from: https://www.eurocontrol.int/sites/default/files/2021-05/eurocontrol-point-merge-guide-v1-4.pdf.

[15]

A. I. HammouriM. S. BraikM. A. Al-Betar and M. A. Awadallah, ISA: A hybridization between iterated local search and simulated annealing for multiple-runway aircraft landing problem, Neural Computing and Applications, 32 (2020), 11745-11765.  doi: 10.1007/s00521-019-04659-y.

[16]

International Civil Aviation Organization (ICAO), Global Air Traffic Management Operational Concept, Report, (2005).

[17]

International Civil Aviation Organization (ICAO), Doc 9882 - Manual on ATM Requirements, Report, (2008).

[18]

X.-P. JiX.-B. CaoW.-B. Du and K. Tang, An evolutionary approach for dynamic single-runway arrival sequencing and scheduling problem, Soft Computing, 21 (2017), 7021-7037.  doi: 10.1007/s00500-016-2241-8.

[19]

X.-P. JiX.-B. Cao and K. Tang, Sequence searching and evaluation: A unified approach for aircraft arrival sequencing and scheduling problems, Memetic Computing, 8 (2016), 109-123.  doi: 10.1007/s12293-015-0172-z.

[20]

S. J. LandryT. FarleyT. Hoang and B. Stein, A distributed scheduler for air traffic flow management, Journal of Scheduling, 15 (2012), 537-551.  doi: 10.1007/s10951-012-0271-3.

[21]

W. MaB. XuM. Liu and H. Huang, An efficient algorithm based on sparse optimization for the aircraft departure scheduling problem, Computational and Applied Mathematics, 35 (2016), 371-387.  doi: 10.1007/s40314-014-0195-y.

[22]

A. A. A. MahmudSa takshi and W. Jeberson, Aircraft Llanding scheduling using embedded flower pollination algorithm, International Journal of Parallel Programming, 48 (2020), 771-785.  doi: 10.1007/s10766-018-0602-x.

[23]

C. NtakoliaH. Caceres and J. Coletsos, A dynamic integer programming approach for free flight air traffic management (ATM) scenario with 4D-trajectories and energy efficiency aspects, Optimization Letters, 14 (2020), 1659-1680.  doi: 10.1007/s11590-019-01458-1.

[24]

C. Ntakolia, A. Kalimeri and J. Coletsos, A two-level hierarchical framework for air traffic flow management, International Journal of Decision Support Systems, 4 (2021), 271–292, available from https://www.researchgate.net/profile/Charis-Ntakolia/publication/350600305_A_two-level_hierarchical_framework_for_air_traffic_flow_management/links/60d1f00945851566d5802d92/A-two-level-hierarchical-framework-for-air-traffic-flow-management.pdf?_sg%5B0%5D=started_experiment_milestone&origin=journalDetail.

[25]

R. PrakashJ. Desai and R. Piplani, An optimal data-splitting algorithm for aircraft sequencing on a single runway, Annals of Operations Research, 309 (2022), 587-610.  doi: 10.1007/s10479-021-04351-2.

[26]

R. Stolletz, Non-stationary delay analysis of runway systems, OR Spectrum, 30 (2008), 191-213.  doi: 10.1007/s00291-007-0099-y.

[27]

K. TastambekovS. PuechmorelD. Delahaye and C. Rabut, Aircraft trajectory forecasting using local functional regression in Sobolev space, Transportation Research Part C Emerging Technologies, 39 (2014), 1-22.  doi: 10.1016/j.trc.2013.11.013.

[28]

B.-C. Zhang, Flight-based congestion pricing considering equilibrium flights in airport airside, Journal of the Operations Research Society of China, 8 (2020), 477-491.  doi: 10.1007/s40305-020-00306-9.

show all references

References:
[1]

J. A. D. AtkinE. K. BurkeJ. S. Greenwood and D. Reeson, An examination of take-off scheduling constraints at London heathrow airport, Public Transport, 1 (2009), 169-187.  doi: 10.1007/s12469-009-0011-z.

[2]

Y.-H. ChangS. SolakJ.-P. B. Clarke and E. L. Johnson, Models for single-sector stochastic air traffic flow management under reduced airspace capacity, Journal of the Operational Research Society, 67 (2016), 54-67.  doi: 10.1057/jors.2015.53.

[3]

E. P. Gilbo, Optimizing airport capacity utilization in air traffic flow management subject to constraints at arrival and departure fixes, IEEE Transactions on Control Systems Technology, 5 (1997), 490-503.  doi: 10.1109/87.623035.

[4]

A. K. Agogino and K. Tumer, A multiagent approach to managing air traffic flow, Autonomous Agents and Multi-Agent Systems, 24 (2012), 1-25.  doi: 10.1007/s10458-010-9142-5.

[5]

J. A. D. AtkinE. K. Burke and J. S. Greenwood, A comparison of two methods for reducing take-off delay at London Heathrow airport, Journal of Scheduling, 14 (2011), 409-421.  doi: 10.1007/s10951-011-0228-y.

[6]

J. A. D. AtkinE. K. BurkeJ. S. Greenwood and D. Reeson, On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport, Journal of Scheduling, 11 (2008), 323-331.  doi: 10.1007/s10951-008-0065-9.

[7]

J. A. BennellM. Mesgarpour and C. N. Potts, Airport runway scheduling, Annals of Operations Research, 204 (2013), 249-270.  doi: 10.1007/s10479-012-1268-1.

[8]

L. BiancoP. Dell'Olmo and S. Giordani, Scheduling models for air traffic control in terminal areas, Journal of Scheduling, 9 (2006), 223-253.  doi: 10.1007/s10951-006-6779-7.

[9]

M. F. Bongo and L. A. Ocampo, Exploring critical attributes during air traffic congestion with a fuzzy DEMATEL–ANP technique: A case study in Ninoy Aquino International Airport, Journal of Modern Transportation, 26 (2018), 147-161.  doi: 10.1007/s40534-017-0150-x.

[10]

J. Coletsos and C. Ntakolia, Air traffic management and energy efficiency: The free flight concept, Energy Systems, 67 (2017), 709-726.  doi: 10.1007/s12667-015-0165-5.

[11]

C. D'ApiceC. De NicolaR. Manzo and V. Moccia, Optimal scheduling for aircraft departures, Journal of Ambient Intelligence and Humanized Computing, 5 (2014), 799-807.  doi: 10.1007/s12652-014-0223-1.

[12]

P. L. de MatosB. Chen and R. J. Ormerod, Optimisation models for re-routing air traffic flows in Europe, Journal of the Operational Research Society, 52 (2001), 1338-1349.  doi: 10.1057/palgrave.jors.2601224.

[13]

Y. DongZ. LuY. LiuQ. Zhang and D. Wu, China's corridors-in-the-sky design and space-time congestion identification and the influence of air routes' traffic flow, Journal of Geographical Sciences, 29 (2019), 1999-2014.  doi: 10.1007/s11442-019-1701-3.

[14]

EUROCONTROL, Point Merge implementation-simplifying and enhancing arrival operations with closed loop sequencing, Report, (2021), available from: https://www.eurocontrol.int/sites/default/files/2021-05/eurocontrol-point-merge-guide-v1-4.pdf.

[15]

A. I. HammouriM. S. BraikM. A. Al-Betar and M. A. Awadallah, ISA: A hybridization between iterated local search and simulated annealing for multiple-runway aircraft landing problem, Neural Computing and Applications, 32 (2020), 11745-11765.  doi: 10.1007/s00521-019-04659-y.

[16]

International Civil Aviation Organization (ICAO), Global Air Traffic Management Operational Concept, Report, (2005).

[17]

International Civil Aviation Organization (ICAO), Doc 9882 - Manual on ATM Requirements, Report, (2008).

[18]

X.-P. JiX.-B. CaoW.-B. Du and K. Tang, An evolutionary approach for dynamic single-runway arrival sequencing and scheduling problem, Soft Computing, 21 (2017), 7021-7037.  doi: 10.1007/s00500-016-2241-8.

[19]

X.-P. JiX.-B. Cao and K. Tang, Sequence searching and evaluation: A unified approach for aircraft arrival sequencing and scheduling problems, Memetic Computing, 8 (2016), 109-123.  doi: 10.1007/s12293-015-0172-z.

[20]

S. J. LandryT. FarleyT. Hoang and B. Stein, A distributed scheduler for air traffic flow management, Journal of Scheduling, 15 (2012), 537-551.  doi: 10.1007/s10951-012-0271-3.

[21]

W. MaB. XuM. Liu and H. Huang, An efficient algorithm based on sparse optimization for the aircraft departure scheduling problem, Computational and Applied Mathematics, 35 (2016), 371-387.  doi: 10.1007/s40314-014-0195-y.

[22]

A. A. A. MahmudSa takshi and W. Jeberson, Aircraft Llanding scheduling using embedded flower pollination algorithm, International Journal of Parallel Programming, 48 (2020), 771-785.  doi: 10.1007/s10766-018-0602-x.

[23]

C. NtakoliaH. Caceres and J. Coletsos, A dynamic integer programming approach for free flight air traffic management (ATM) scenario with 4D-trajectories and energy efficiency aspects, Optimization Letters, 14 (2020), 1659-1680.  doi: 10.1007/s11590-019-01458-1.

[24]

C. Ntakolia, A. Kalimeri and J. Coletsos, A two-level hierarchical framework for air traffic flow management, International Journal of Decision Support Systems, 4 (2021), 271–292, available from https://www.researchgate.net/profile/Charis-Ntakolia/publication/350600305_A_two-level_hierarchical_framework_for_air_traffic_flow_management/links/60d1f00945851566d5802d92/A-two-level-hierarchical-framework-for-air-traffic-flow-management.pdf?_sg%5B0%5D=started_experiment_milestone&origin=journalDetail.

[25]

R. PrakashJ. Desai and R. Piplani, An optimal data-splitting algorithm for aircraft sequencing on a single runway, Annals of Operations Research, 309 (2022), 587-610.  doi: 10.1007/s10479-021-04351-2.

[26]

R. Stolletz, Non-stationary delay analysis of runway systems, OR Spectrum, 30 (2008), 191-213.  doi: 10.1007/s00291-007-0099-y.

[27]

K. TastambekovS. PuechmorelD. Delahaye and C. Rabut, Aircraft trajectory forecasting using local functional regression in Sobolev space, Transportation Research Part C Emerging Technologies, 39 (2014), 1-22.  doi: 10.1016/j.trc.2013.11.013.

[28]

B.-C. Zhang, Flight-based congestion pricing considering equilibrium flights in airport airside, Journal of the Operations Research Society of China, 8 (2020), 477-491.  doi: 10.1007/s40305-020-00306-9.

Figure 1.  Simplified configuration of the approach airspace
Figure 2.  The altitude profiles and speed profiles of departures
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
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
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
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
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)
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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