Terminal Control Area Complexity Measurement Using Simulation Model

Authors

DOI:

https://doi.org/10.31436/iiumej.v24i1.2223

Keywords:

terminal control area, air traffic complexity, simulation model, analytic hierarchy process

Abstract

Traffic density in the terminal control area will increase flight safety risks. One effort to reduce the risk is to minimize the controller’s workload when affected by air traffic complexity. This research uses a simulation model to measure air traffic complexity in terminal control areas. The aircraft performance model has been constructed from ADS-B data and represents the aircraft movement in the terminal control area of Soekarno-Hatta International Airport. The simulation model can detect and resolve conflicts to keep separations between aircraft at a specified minimum separation limit. Air traffic complexity measurement uses several indicators, i.e., aircraft density, number of climbing and descending aircraft, aircraft type mixing, conflict control, aircraft speed difference, and controller communication. The weighting factor for each indicator has been obtained from Jakarta Air Traffic Service Center (JATSC) controller perception using an analytic hierarchy process. The simulation results show that the variation of resolution type affects the complexity level significantly. The results of this study can be used as consideration for improving air traffic control procedures and air space structures.

ABSTRAK: Kepadatan trafik di kawasan terminal kawalan bakal menyebabkan peningkatan risiko keselamatan penerbangan. Salah satu cara bagi mengurangkan risiko adalah dengan meminimumkan beban kerja pengawal yang terlibat dengan kesesakan trafik udara. Kajian ini menggunakan model simulasi bagi mengukur kesesakan trafik udara di kawasan terminal kawalan. Model pretasi pesawat telah dibina menggunakan data ADS-B dan ini mewakili pergerakan pesawat di terminal kawalan lapangan terbang antarabangsa Soekarno-Hatta. Model simulasi ini dapat mengesan konflik dan membuat resolusi bagi mengekalkan penjarakan antara pesawat mengikut had penjarakan  minimum yang ditetapkan. Beberapa indikator telah digunakan bagi mengukur kerumitan trafik udara, iaitu: ketumpatan pesawat, bilangan pesawat mendaki dan menurun, jenis pesawat, kawalan konflik, perbezaan kelajuan pesawat dan pengawal komunikasi. Faktor pemberat bagi setiap indikator telah diperoleh daripada pengawal persepsi Pusat Servis Trafik Udara Jakarta (JATSC) menggunakan proses analisis hierarki. Keputusan simulasi menunjukkan pelbagai jenis resolusi mempengaruhi tahap kerumitan dengan ketara. Hasil kajian ini boleh digunakan bagi menambah baik prosedur kawalan trafik udara dan struktur ruang udara.

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Author Biographies

Rully Medianto, Bandung Institute of Technology

Faculty of Mechanical and Aerospace Engineering, Bandung Insitute of Technology, Bandung, Indonesia

Faculty of Aerospace Engineering, Adisutjipto Institute of Aerospace Technology., Yogyakarta, Indonesia

 

Yazdi Ibrahim Jenie, Bandung Institute of Technology

Faculty of Mechanical and Aerospace Engineering, Bandung Insitute of Technology, Bandung, Indonesia

Hisar Manongam Pasaribu, Bandung Institute of Technology

Faculty of Mechanical and Aerospace Engineering, Bandung Insitute of Technology, Bandung, Indonesia

Hari Muhammad, Bandung Institute of Technology

Faculty of Mechanical and Aerospace Engineering, Bandung Insitute of Technology, Bandung, Indonesia

References

Bouarfa S. (2015) Agent-Based Modelling and Simulation of Safety and Resilience in Air Transportation. Doctoral Dissertation, Delft University of Technology. https://doi.org/10.4233/uuid:b676db6c-ed86-4b42-9940-9b90b94651f1

Netjasov F, Janic M, Tosic V. (2009) The Future Air Transport System: Looking for Generic Metrics of Complexity for Terminal Airspace. 88th Transportation Research Board (TRB) Annual Meeting, Washington DC, USA.

Netjasov F, Janic M, Tosic V. (2011) Developing a Generic Metric of Terminal Airspace Traffic Complexity. Transportmetrica 7(5), 369-394. https://doi.org/10.1080/18128602.2010.505590 DOI: https://doi.org/10.1080/18128602.2010.505590

Diaconu AG, Stanciu V, Pleter OT. (2014) Air Traffic Complexity Metric for En-Route and Terminal Areas. U.P.B. Sci. Bull., Series D, Vol. 76, Iss. 1.

Djokic J, Lorenz B, Fricke H. (2010) Air Traffic Control Complexity as Workload Driver. Transportation Research Part C Emerging Technologies 18(6):930-936. https://doi.org/10.1016/j.trc.2010.03.005 DOI: https://doi.org/10.1016/j.trc.2010.03.005

Mogford RH, Guttman JA, Morrow SL, Kopardekar P. (1995) The Complexity Construct in Air Traffic Control: A Review and Synthesis of the Literature. Report N0. DOT/FAA/CT-TN95/22. U.S. Department of Transportation, Federal Aviation Administration, Office of Aviation Research, Washington, D.C.

Dervic A, Rank A. (2015) ATC complexity measures: Formulas measuring workload and complexity at Stockholm TMA. Department of Science and Technology, Linköping University, Sweden.

Arad BA, Golden BT, Grambart JE, Mayfield CE, Van Saun HR. (1963) Control Load, Control Capacity, and Optimal Sector Design (Report No. RD64-16). Federal Aviation Administration, Atlantic City, NJ.

Grossberg M. (1989) Relation of Sector Complexity to Operational Errors. Quarterly Report of the FAA Office of Air Traffic Evaluations and Analysis.

Mogford RH, Murphy ED, Yastrop G, Guttman JA, Roske-Hofstrand R. (1993) The Application of Research Techniques for Documenting Cognitive Processes in Air Traffic Control (Report No. DOT/FAA/CT-TN93/39). Federal Aviation Administration, Atlantic City, NJ. DOI: https://doi.org/10.1037/e645182007-001

Pawlak WS, Brinton CR, Crouch K, Lancaster KM. (1996) A Framework for the Evaluation of Air Traffic Control Complexity. American Institute of Aeronautics and Astronautics, Inc. DOI: https://doi.org/10.2514/6.1996-3856

Laudeman I, Shelden S, Brannstrom R, Brasil C. (1998) Dynamic Density: An Air Traffic Management Metric. Ames Research Center.

Majumdar A, Ochieng WY. (2000) The Factor Affecting Air Traffic Controller Workload: A Multivariate Analysis Based upon Simulation Modelling of Controller Workload. Center for Transport Studies.

Chatterji GB, Sridhar B. (2001) Measures for Air Traffic Controller Workload Prediction. First AIAA Aircraft Technology, Integration and Operations Forum. https://doi.org/10.2514/6.2001-5242 DOI: https://doi.org/10.2514/6.2001-5242

Koros A, Della Rocco PS, Panjwani G, Ingurgio V, D'Arcy JF. (2003) Complexity in Air Traffic Control Towers: A Field Study. Part 1: Complexity Factors. DOT/FAA/CT-TN03/14. Federal Aviation Agency, Atlantic City, NJ.

Radiši? T, Andraši P, Novak D, Juri?i? B, Antulov-Fantulin B. (2020) Risk Assessment in Air Traffic Management. Edited by P. Castán and J. Alberto. IntechOpen, London. pp 56-83.

Wang H, Song Z, Wen R. (2018) Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach. Journal of Advanced Transportation, vol. 2018 vol. 2018, Article ID 5254289, 15 pages. https://doi.org/10.1155/2018/5254289 DOI: https://doi.org/10.1155/2018/5254289

Andraši P, Radiši? T, D. Novak, and B. Juri?i? (2019) Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks. Traffic & Transportation, 31(4): 377-386. https://doi.org/10.7307/ptt.v31i4.3018 DOI: https://doi.org/10.7307/ptt.v31i4.3018

Medianto R, Pasaribu HM, Muhammad H. (2019) Development of Hybrid Simulation Model of Air Traffic Management in the Terminal Control Area. IOP Conf. Series: Materials Science and Engineering, 645: 012003. https://doi.org/10.1088/1757-899x/645/1/012003 DOI: https://doi.org/10.1088/1757-899X/645/1/012003

Directorate General of Civil Aviation (2019) Nr: The Establishment And Revision of Instrument Flight Procedures at Soekarno Hatta International Airport – Jakarta, AIRAC AIP Supplement 14/19 25 APR 19. Directorate of Air Navigation, Directorate General of Civil Aviation, Republic Of Indonesia. FlightRadar24 Data/History Flights.

Airnav Indonesia (2020) Standard Operating Procedures Air Traffic Services Approach Control Service. Airnav Indonesia Branch of Jakarta Air Traffic Service Center.

Horasio K. (2019) Air Traffic Conflict Resolution Modelling and Analysis in Controlled Airspace. Master's Thesis. Bandung Institute of Technology, Aerospace Engineering Department.

FlightRadar24 Flight Database [https://www.flightradar24.com/data/flights]

METAR/SPECI & Trend Forecast, Aviation Meteorological Information System in Meteorological, Climatology, and Geophysical Agency (BMKG) [http://aviation.bmkg.go.id/web/metar_speci.php]

Pasaribu HM, Medianto R, Jusuf J, Oktafianto R, Atiqah R (2021) ADS-B data processing to develop aircraft kinematics model parameters. AIP Conference Proceedings, 2366: 02001. https://doi.org/10.1063/5.0060611 DOI: https://doi.org/10.1063/5.0060611

Medianto R, Jusuf J, Oktafianto R, Atiqah R, Sembiring J, Pasaribu HM, Jenie YI, Muhammad H. (2021) Stochastic modelling of aircraft flight parameters in terminal control area based on automatic dependent surveillance-broadcast (ADS-B) data. IOP Conference Series: Materials Science and Engineering, 1173: 012053. https://doi.org/10.1088/1757-899X/1173/1/012053 DOI: https://doi.org/10.1088/1757-899X/1173/1/012053

Sargent RG. (2005) Verification and Validation of Simulation Models, Proceedings of the 2005 Winter Simulation Conference, 130-143. https://doi.org/10.1109/wsc.1994.717077 DOI: https://doi.org/10.1109/WSC.1994.717077

Mogford R, Guttman J, Morrow S, and Kopardekar P. (1995) The Complexity Construct in Air Traffic Control: A Review and Synthesis of the Literature, Federal Aviation Administration.

Li-na S, Li Z, Lei Z. (2015) The Sector Capacity Evaluation Considering the Controller's Workloads. International Journal of Control and Automation, 8: 307-324. https://doi.org/10.14257/ijca.2015.8.7.31 DOI: https://doi.org/10.14257/ijca.2015.8.7.31

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Published

2023-01-04

How to Cite

Medianto, R., Naflah Mutiara Adinda, Jenie, Y. I. ., Pasaribu, H. M. ., & Hari Muhammad. (2023). Terminal Control Area Complexity Measurement Using Simulation Model. IIUM Engineering Journal, 24(1), 199–212. https://doi.org/10.31436/iiumej.v24i1.2223

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Section

Mechanical and Aerospace Engineering