Development of a Policy Decision Support Management Model for Air Traffic Management

Hyojin Kim; Taejin Shin

doi:10.7470/jkst.2026.44.4.538

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2026 Vol.44, Issue 4 Preview Page Next Page

Article

Development of a Policy Decision Support Management Model for Air Traffic Management ATM(Air Traffic Management) 정책 의사결정 지원 모형 구축

31 August 2026. pp. 538-568

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Abstract

This study develops a Decision Support Management (DSM) Model to evaluate and forecast the impacts of policy alternatives in Air Traffic Management (ATM) by incorporating system-wide interactions. The framework employs a Full Information Maximum Likelihood (FIML)-based simultaneous equation modeling approach to analyze three measurable performance metrics identified in Korea’s National ATM Reformation And Enhancement (NARAE 2.0): flight time efficiency, on-time performance, and traffic throughput. The analysis draws on daily passenger and cargo flight operations data from four major Korean airports—Incheon, Gimpo, Gimhae, and Jeju—covering the period 2019–2021. The model comprises 44 behavioral equations and 14 definitional equations, estimated through regression and autoregressive specifications, with system-level predictions obtained via the Gauss–Seidel iterative method. Policy simulations were conducted by treating additional Terminal Manoeuvring Area (TMA) flight time as an exogenous shock under three scenarios. Results show that reducing additional TMA time improves arrival punctuality, slightly enhances departure punctuality, and shortens overall flight time. The influence of Incheon International Airport extends to Gimpo, Gimhae, and Jeju, whereas the reverse effect is limited, confirming Incheon’s systemic dominance. The contributions of this research are twofold. First, it presents an integrated framework that captures the interdependencies among flight time efficiency, on-time performance, and traffic throughput, moving beyond single-airport or single-metric analyses. Second, it provides a practical decision-support tool by quantifying dynamic multi-airport responses to policy interventions. Limitations remain, as factors such as fuel efficiency, cruising altitude, air traffic controller characteristics, and detailed airspace operations were excluded due to data limitations. Future research should expand the DSM by incorporating these dimensions, thereby enabling more comprehensive evaluations of safety and environmental impacts.

Keywords

air traffic management

decision support management

flight-time efficiency

on-time performance

throughput

simultaneous equation model

본 연구는 항공교통관리(Air Traffic Management, ATM)에서 정책 대안의 효과를 사전에 예측·평가하기 위하여 시스템 전반의 상호작용을 반영한 정책 의사결정지원모형(Decision Support Management, DSM)을 구축하였다. DSM은 연립방정식체계 모형(FIML)을 기반으로 하여, 국가항행계획(NARAE 2.0)에서 제시한 성과목표 중 데이터 확보가 가능한 비행시간 효율성, 정시성, 처리교통량을 내생적으로 동시에 추정하였다. 분석은 인천·김포·김해·제주 4개 공항의 여객·화물기 출·도착 항공편 일별 자료(2019–2021)를 활용하였으며, 항공·공역·기상·운영 변수를 포함하여 총 44개의 행태방정식과 14개의 정의식으로 모형을 구성하였다. 공항 및 출도착 특성을 반영하여 회귀모형과 자기회귀모형을 적용하였고, Gauss–Seidel 반복법을 통해 시스템 수준의 예측치를 산출·수렴하였다. 정책효과 분석에서는 추가 TMA(Terminal Manoeuvring Area, 접근관제구역 ) 비행시간을 외생 변수로 설정하여 세 가지 시나리오를 모의실험하였다. 그 결과, 추가 TMA 비행시간 감소는 도착 정시성 개선과 더불어 출발 정시성 및 비행시간 효율성에도 긍정적 효과를 나타냈다. 특히 인천공항의 변화는 김포·김해·제주공항에 파급되었으나 역방향 영향은 제한적이었으며, 이를 통해 인천공항의 시스템 지배력이 확인되었다. 본 연구의 기여는 첫째, 개별 지표나 단일 공항에 국한되지 않고, 비행시간 효율성·정시성·처리교통량의 상호 의존 관계를 통합적으로 규명한 점이다. 둘째, 정책 변수 변화에 따른 다공항 간 동태적 반응을 정량적으로 시뮬레이션하여 ATM 정책결정자에게 실무적 의사결정 지원 도구를 제시한 점이다. 다만, 연료 효율·순항 고도·관제사 요인·세부 공역 운영 등은 데이터 한계로 제외되었다. 향후 데이터 확충과 모형 고도화를 통해 안전성과 환경영향까지 포함하는 확장형 DSM으로 발전시킬 필요가 있다.

키워드

항공교통관리

의사결정지원모형

비행시간 효율성

정시성

처리교통량

연립방정식 체계모형

References

Allan S. S., Gaddy S. G., Evans J. E. (2001), Delay Causality and Reduction at the New York City Airports using Terminal Weather Information Systems (No.ATC-291), Cambridge, Mass, USA: Lincoln Laboratory, Massachusetts Institute of Technology.

Burden R. L., Faires J. D. (1997), Numerical Analysis, brooks.

Fair R. C. (1984), Specification, Estimation, and Analysis of Macroeconometric Models, Harvard University Press.

Gilbo E. P. (2002), Optimizing Airport Capacity Utilization in Air Traffic Flow Management Subject to Constraints at Arrival and Departure Fixes, IEEE Transactions on Control Systems Technology, 5(5), IEEE, 490-503.

10.1109/87.623035

Greene W. H. (2012), Econometric Analysis, 7th Ed., Prentice Hall.

Hao L., Hansen M., Zhang Y., Post J. (2014), New York, New York: Two Ways of Estimating the Delay Impact of New York Airports, Transportation Research Part E: Logistics and Transportation Review, 70, Elsevier, 245-260.

10.1016/j.tre.2014.07.004

ICAO D. (2009), 9883—Manual on Global Performance of the Air Navigation System, ICAO: Montreal, QC, Canada.

Jeong M. S., Eun Y. J., Kim H. K., Jeon D. K. (2017), A Study on Taxi Route Extraction Based on a Node-Link Model for Aircraft Movements on Airport Surface, Journal of the Korean Society for Aviation and Aeronautics, 25(3), 51-60.

Kim J. H., Seo Y. S. (2020), A Study on Variable Taxi Time Prediction, Journal of Transport Research, 27(3), The Korea Transport Institute, 35-49.

Kim S. Y., Jeon D. K., Eun Y. J. (2018), A Development of Data-Driven Aircraft Taxi Time Prediction Algorithm, Journal of the Korean Society for Aviation and Aeronautics, 26(2), 39-46.

10.12985/KSAA.2018.26.2.039

Lerman S. R. (1976), Location, Housing, Automobile Ownership, and Mode to Work: A Joint Choice Model, Transportation Research Record, 610, 6-11.

Nayak N. (2012), Estimation of the Impact of Single Airport and Multi-Airport System Delay on the National Airspace System using Multivariate Simultaneous Models, University of South Florida.

Park S. D., Lee Y. H., Chang J. W. (2018), A Study on Computing the Optimal Number of Attached Stands in the Airport, Journal of the Korean Society for Aviation and Aeronautics, 26(4), 13-19.

10.12985/ksaa.2018.26.4.013

Ravizza S., Atkin J. A., Maathuis M. H., Burke E. K. (2013), A Combined Statistical Approach and Ground Movement Model for Improving Taxi Time Estimations at Airports, Journal of the Operational Research Society, 64(9), Taylor & Francis, 1347-1360.

10.1057/jors.2012.123

Seok J. U. (2018), Development of a Cost Prediction Model on Flight Operation Service Delay, Hanyang University, Master's degree thesis.

Shin T. J., Kim H. J. (2024), Big Data Preprocessing for a Policy Decision Support of Air Traffic Management (ATM), KIISE Transactions on Computing Practices, 30(9), 440-457.

10.5626/KTCP.2024.30.9.440

Zhang Y., Wang Q. (2017), Methods for Determining Unimpeded Aircraft Taxiing Time and Evaluating Airport Taxiing Performance, Chinese Journal of Aeronautics, 30(2), 523-537.

10.1016/j.cja.2017.01.002

Information

Publisher :Korean Society of Transportation
Publisher(Ko) :대한교통학회
Journal Title :Journal of Korean Society of Transportation
Journal Title(Ko) :대한교통학회지
Volume : 44
No :4
Pages :538-568
Received Date : 2026-01-29
Revised Date : 2026-03-03
Accepted Date : 2026-04-20
DOI :https://doi.org/10.7470/jkst.2026.44.4.538

Journal of Korean Society of Transportation ISSN:1229-1366(Print) 2234-4217(Online) 대한교통학회지

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