journal article·2024-04-24Open access

How Has the Concept of Air Traffic Complexity Evolved? Review and Analysis of the State of the Art of Air Traffic Complexity

Francisco Pérez Moreno, Víctor Fernando Gómez Comendador, Raquel Delgado-Aguilera Jurado, María Zamarreño Suárez, Bruno Antulov-Fantulin, Rosa María Arnaldo Valdés

Applied Sciences· Volume 14· Issue 9

Abstract

Air traffic complexity is an indicator that allows air traffic controllers to understand the airspace situation. Controllers need support tools to reduce their workload. For this reason, complexity is a parameter that is being studied more and more, as it makes it possible to know a large amount of information about air traffic. In this article, we perform a bibliometric analysis in the field of air traffic complexity. Through Web of Science (WoS), a collection of complexity-related articles from 2001 to 2022 is compiled. Subsequently, the bibliometric analysis itself is performed. Then, a summary of five main contributions is presented, identifying the strengths and weaknesses of the contributions, and thus the topic. The results of the bibliometric analysis show that future air traffic complexity indicators should consider aircraft trajectories but also take into account other aspects, such as regulations. In addition, future complexity indicators should introduce artificial intelligence predictions to foresee areas of conflict in airspace but taking into account the main limitations, such as uncertainty of the air traffic trajectories. This study helps in the study of complexity due to being able to know previous studies in a summarised form and being able to draw conclusions on future lines.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Air Traffic Management and Optimization · Aviation Industry Analysis and Trends · Human-Automation Interaction and Safety

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Aerospace Systems, Air Transport and Airports, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain

Department of Aeronautics, Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia

References (52)

Show source-provided references
1. Evaluating the human performance factors of air traffic control in Thailand using Fuzzy Multi Criteria Decision Making method10.1016/j.jairtraman.2019.101708 ↗
2. Identifying similar days for air traffic management10.1016/j.jairtraman.2017.06.005 ↗
3. EUROCONTROL (2013). Challanges of Growth 2013: Summary Report, EUROCONTROL.
4. Airport traffic complexity and environment efficiency metrics for evaluation of ATM measures10.1016/j.jairtraman.2014.11.008 ↗
5. Learning air traffic as images: A deep convolutional neural10.1155/2021/6457246 ↗
6. Measuring air traffic complexity based on small samples10.1016/j.cja.2017.04.018 ↗
7. Reduction of Air Traffic Complexity Using Trajectory-Based Operations and Validation of Novel Complexity Indicators10.1109/TITS.2017.2666087 ↗
8. Wang, F., and Zhao, L. (2019, January 3–5). Complexity Analysis of Air Traffic Flow Based on Sample Entropy. Proceedings of the 2019 Chinese Control and Decision Conference (CCDC), Nanchang, China.10.1109/CCDC.2019.8832997 ↗
9. Air-traffic complexity resolution in multi-sector planning10.1016/j.jairtraman.2007.05.001 ↗
10. Gianazza, D., and Guittet, K. (2006, January 15–19). Selection and evaluation of air traffic complexity metrics. Proceedings of the 2006 IEEE/AIAA 25th Digital Avionics Systems Conference, Portland, OR, USA.10.1109/DASC.2006.313710 ↗
11. Toward Air Traffic Complexity Assessment in New Generation Air Traffic Management Systems10.1109/TITS.2011.2113175 ↗
12. Dynamic model to characterise sectors using machine learning techniques10.1108/AEAT-11-2021-0330 ↗
13. Gianazza, D. (2007, January 2–5). Airspace configuration using air traffic complexity metrics. Proceedings of the 7th FAA/Europe Air Traffic Management Research and Development Seminar, Barcelona, Spain.
14. ATCEM: A synthetic model for evaluating air traffic complexity10.1002/atr.1321 ↗
15. Determining Air Traffic Complexity challenges and future development10.7307/ptt.v32i4.3401 ↗
16. Moreno, F.P., Comendador, V.G., Jurado, R.D.-A., Suárez, M.Z., Janisch, D., and Valdés, R.A. (2022). Determination of Air Traffic Complexity Most Influential Parameters Based on Machine Learning Models. Symmetry, 14.10.3390/sym14122629 ↗
17. On the ramifications of airspace bans in aero-political conflicts: Towards a country importance ranking10.1016/j.tranpol.2023.04.003 ↗
18. Examining the Economic Costs of the 2003 Halloween Storm Effects on the North Hemisphere Aviation Using Flight Data in 201910.1029/2022SW003381 ↗
19. Data-driven trajectory prediction with weather uncertainties: A Bayesian deep learning approach10.1016/j.trc.2021.103326 ↗
20. Ali, B. (2013). A Safety Assessment Framework for Automatic Dependent Surveillance Broadcast (ADS-B) and Its Potential Impact on Aviation Safety, Centre for Transport studies, Department of Civil and Environmental Engineering, Imperial College London.
21. Cooperative surveillance systems and digital-technology enabler for a real-time standard terminal arrival schedule displacement10.1016/j.aei.2021.101402 ↗
22. Twenty five years of the journal of travel & tourism marketing: A bibliometric ranking10.1080/10548408.2018.1487368 ↗
23. Thirty Years of the International Journal of Intelligent Systems: A Bibliometric Review
24. Yang, W., Zhang, J., and Ma, R. (2020). The Prediction of Infectious Diseases: A Bibliometric Analysis. Int. J. Environ. Res. Public Health, 17.10.3390/ijerph17176218 ↗
25. A bibliometric review of finance bibliometric papers10.1016/j.frl.2021.102520 ↗
26. The big picture on Instagram research: Insights from a bibliometric analysis10.1016/j.tele.2022.101876 ↗
27. A bibliometric analysis of airport service quality10.1016/j.jairtraman.2022.102273 ↗
28. Valdés, R.A., Burmaoglu, S., Tucci, V., de Costa, L.B., Mattera, L., and Comendador, V.G. (2019). Flight Path 2050 and ACARE Goals for Maintaining and Extending Industrial Leadership in Aviation: A Map of the Aviation Technology Space. Sustainability, 11.10.3390/su11072065 ↗
29. What can we learn from the JATM literature for the future of aviation post COVID-19?—A bibliometric and visualization analysis10.1016/j.jairtraman.2020.101916 ↗
30. Airport capacity management: A review and bibliometric analysis10.1016/j.jairtraman.2020.102010 ↗
31. Author cocitation: A literature measure of intellectual structure10.1002/asi.4630320302 ↗
32. Twenty Years of Airport Efficiency—A Bibliometric Analysis
33. Castañeda, K., Sánchez, O., Herrera, R., and Mejía, R. (2022). Highway Planning Trends: A Bibliometric Analysis. Sustainability, 14.10.3390/su14095544 ↗
34. Historiographic mapping of knowledge domains literature10.1177/0165551504042802 ↗
35. 20 years after To Err Is Human: A bibliometric analysis of ‘the IOM report’s’ impact on research on patient safety10.1016/j.ssci.2021.105593 ↗
36. Some bibliometric procedures for analyzing and evaluating research fields
37. KeyWords PlusTM Algorithmic Derivative Indexing10.1002/(SICI)1097-4571(199306)44:5<298::AID-ASI5>3.0.CO;2-A ↗
38. Wastewater treatment and emerging contaminants: Bibliometric analysis10.1016/j.chemosphere.2022.133932 ↗
39. Digital Transformation: A Bibliometric Analysis10.4018/JOEUC.302637 ↗
40. Gua, Y., Hunag, Z., Guo, J., Li, H., Guo, X., and Nkeli, M. (2019). Bibliometric Analysis on Smart Cities Research. Sustainability, 11.10.3390/su11133606 ↗
41. A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation10.1109/TITS.2021.3106779 ↗
42. A bibliometric analysis and visualization of blockchain10.1016/j.future.2020.10.023 ↗
43. Hossian, N., Dayarathna, V., Nagahi, M., and Jaradat, R. (2020). Systems Thinking: A Review and Bibliometric Analysis. Systems, 8.10.3390/systems8030023 ↗
44. Electronic stripping interface10.1108/00022660310503075 ↗
45. Describing Airspace Complexity: Airspace Response to Disturbances10.2514/1.36308 ↗
46. Delahaye, D., and Puechmorel, S. (2010, January 15–17). Air traffic complexity based on dynamical systems. Proceedings of the 49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, USA.10.1109/CDC.2010.5718004 ↗
47. Study on evolution characteristics of air traffic situation complexity based on complex network theory10.1016/j.ast.2016.09.016 ↗
48. A knowledge-transfer-based learning framework for airspace operation complexity evaluation10.1016/j.trc.2018.07.008 ↗
49. A new approach to air traffic controller workload measurement and modelling10.1108/00022661111119900 ↗
50. Increasing the resilience of air traffic networks using a network graph theory approach10.1016/j.tre.2015.09.011 ↗
51. Comparison of various temporal air traffic flow management models in critical scenarios10.1016/j.jairtraman.2022.102284 ↗
52. Forecasting workload and airspace configuration with neural networks and tree search methods10.1016/j.artint.2010.03.001 ↗