Identifying Operational Benefits of the Arrival Management System – A KPI-Based Experimental Method by Evaluating Radar Trajectories

  • Songwei Liu Nanjing University of Aeronautics and Astronautics, College of Civil Aviation
  • Junfeng Zhang Nanjing University of Aeronautics and Astronautics, College of Civil Aviation
  • Zihan Peng Nanjing University of Aeronautics and Astronautics, College of Civil Aviation
  • Haipeng Guo Central and Southern Regional Air Traffic Management Bureau of the Civil Aviation Administration of China
  • Anle Pi Hunan Branch of Central and Southern Regional Air Traffic Management Bureau of the Civil Aviation Administration of China
Keywords: arrival management, key performance indicator, benefit evaluation, air traffic management

Abstract

The arrival management (AMAN) system is a decision support tool for air traffic controllers to establish and maintain the landing sequence for arrival aircraft. The original intention of designing the AMAN system is to improve the efficiency of air traffic management (ATM), but few studies are investigating the operational benefits of this system based on key performance indicators (KPIs) and evaluating actual data in a real-time environment. The main purpose of this paper is to propose a KPI based transferable comparative analysis method for identifying the operational benefits of the AMAN through radar trajectories. Firstly, six KPIs are established from a joint study of the mainstream ATM performance frameworks worldwide. Secondly, appropriate evaluation technique approaches are determined according to the characteristics of each KPI. Finally, a Chinese metropolitan airport is taken for the case study, and three periods are defined to form data samples with high similarity for comparative experiments. The results validate the feasibility of the proposed method and find comprehensive performance improvements in arrival operations under the effects of the AMAN system.

References

International Civil Aviation Organization. The aviation system block upgrades – The framework for global harmonization. Montreal, Quebec, Canada: ICAO; 2016.

Guzhva VS, Abdelghany A, Lipps T. Experimental approach to NextGen benefits estimation: A case of single- airline aircraft arrival management system. Journal of Air Transport Management. 2014;35: 108-116. DOI: 10.1016/j.jairtraman.2013.12.003

European Organization for the Safety of Air Navigation. Review of current KPIs and proposal for new ones. Brussels, Belgium: EUROCONTROL; 2017.

European Organization for the Safety of Air Navigation. Arrival Manager, implementation guidelines and lessons learned. Brussels, Belgium: EUROCONTROL; 2010.

Bennell JA, Mesgarpour M, Potts CN. Airport runway scheduling. Annals of Operation Research. 2013;204(1): 204-249. DOI: 10.1007/s10479-012-1268-1

Zhang JF, et al. Criteria selection and multi-objective optimization of aircraft landing problem. Journal of Air Transport Management. 2020;82: 101734. DOI: 10.1016/j.jairtraman.2019.101734

Bennell JA, Mesgarpour M, Potts CN. Dynamic scheduling of aircraft landings. European Journal of Operational Research. 2017;258(1): 315-327. DOI: 10.1016/j.ejor.2016.08.015

Vadlamani S, Hosseini S. A novel heuristic approach for solving aircraft landing problem with single runway. Journal of Air Transport Management. 2014;40: 144-148. DOI: 10.1016/j.jairtraman.2014.06.009

Lieder A, Briskorn D, Stolletz R. A dynamic programming approach for the aircraft landing problem with aircraft classes. European Journal of Operational Research. 2015;243(1): 61-69. DOI: 10.1016/j.ejor.2014.11.027

Solving G, Clark JP. Scheduling of airport runway operations using stochastic branch and bound methods. Transportation Research Part C: Emerging Technologies. 2014;45(8): 119-137. DOI: 10.1016/j.trc.2014.02.021

Zhang JF, et al. A new meta-heuristic approach for aircraft landing problem. Transactions of Nanjing University of Aeronautics and Astronautics. 2020;37(2): 197-208. DOI: 10.16356/j.1005•1120.2020.02.003

Hu XB, Di Paolo E. Binary-representation-based genetic algorithm for aircraft arrival sequencing and scheduling. IEEE Transaction on Intelligent Transportation System. 2008;9(2): 301-310. DOI: 10.1109/TITS.2008.922884

Salehipour A, Modarres M, Naeni LM. An efficient hybrid meta-heuristic for aircraft landing problem. Computers & Operations Research. 2013;40(1): 207-213. DOI: 10.1016/j.cor.2012.06.004

Girish BG. An efficient hybrid particle swarm optimization algorithm in a rolling horizon framework for the aircraft landing problem. Applied Soft Computing. 2016;44: 200-221. DOI: 10.1016/j.asoc.2016.04.011

Zhan ZH, et al. An efficient ant colony system based on recording horizon control. IEEE Transaction on Intelligent Transportation System. 2010;11(2): 399-412. DOI: 10.1109/TITS.2010.2044793

Harikiopoulo D, Neogi N. Polynomial-time feasibility condition for multiclass aircraft sequencing on a single-runway airport. IEEE Transaction on Intelligent Transportation System. 2011;12(1): 2-13. DOI: 10.1109/TITS.2010.2055856

Sama M, et al. Scheduling models for optimal aircraft traffic control at busy airports: Tardiness, priorities, equity and violations considerations. Omega-International Journal of Management Science. 2017;67: 81-98. DOI: 10.1016/j.omega.2016.04.003

Prevot T, et al. Efficient arrival management utilizing ATC and aircraft automation. In: International Conference on Human-Computer Interaction in Aeronautics, HCI-Aero 2000, September 2000, Toulouse, France. Toulouse, France: ACM; 2000. p. 1-11.

Andersson K, et al. Optimization-based analysis of collaborative airport arrival planning. Transportation Science. 2003;37(4): 422-433. DOI: 10.1287/trsc.37.4.422.23274

Meyn LA, Erzberger H. Airport arrival capacity benefits due to improved scheduling accuracy. In: AIAA 5th Aviation Technology, Integration, and Operations Conference, 26-28 September 2005, Arlington, Virginia, USA. Reston, VA, USA: AIAA; 2005. p. 7376-7386.

Miyazawa Y, et al. Potential benefits of arrival time assignment: Dynamic programming trajectory optimization applied to the Tokyo international airport. In: 11th USA/Europe Air Traffic Management Research and Development Seminar, 23-26 June 2015, Lisbon, Portugal. Lisbon, Portugal: FAA & EUROCONTROL; 2015.

European Organization for the Safety of Air Navigation. Performance review body of Single European Sky. Brussels, Belgium: EUROCONTROL; 2016.

International Civil Aviation Organization. Manual on global performance of the air navigation system. Doc 9883. Montreal, Quebec, Canada: ICAO; 2007.

Civil Air Navigation Services Organization. Recommended KPIs for measuring air navigation service providers operational performance. Hoofddorp, Netherland: CANSO; 2015.

European Organization for the Safety of Air Navigation. Performance framework. Brussels, Belgium: EUROCONTROL; 2017.

Federal Aviation Administration of USA. NEXTGEN performance snapshots. Washington D.C., USA: FAA; 2015.

Olive X, Morio J. Clustering of air traffic flows around airports. Aerospace Science Technology. 2019;84: 776-781. DOI: 10.1016/j.ast.2018.11.031

He XF, Cai D, Niyogi P. Laplacian score for feature selection. In: Weiss Y, Scholkopf B, Platt JC. (eds.) NIPS’05: Proceedings of the 18th International Conference on Neural Information Processing Systems: Natural and Synthetic, NIPS’05, December 2005, Vancouver, British Columbia, Canada. Cambridge, Massachusetts, USA: MIT Press; 2006. p. 507-514.

Daniel WW. Spearman rank correlation coefficient. Applied Nonparametric Statistics. 2nd ed. Boston: PWS-Kent Publishing Company; 1990. p. 358-365.

Published
2021-10-08
How to Cite
1.
Liu S, Zhang J, Peng Z, Guo H, Pi A. Identifying Operational Benefits of the Arrival Management System – A KPI-Based Experimental Method by Evaluating Radar Trajectories. Promet [Internet]. 2021Oct.8 [cited 2024Apr.26];33(5):633-45. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/3786
Section
Articles