4D Trajectory Conflict Detection and Resolution Using Decision Tree Pruning Method



4-Dimensional Trajectory, Conflict Detection and Resolution, Decision Tree Pruning Method, Not Only SQL


The aviation community develops Trajectory Based Operations (TBO) as an advancement in Air Traffic Management (ATM). There is still the need for an efficient scheme to present the trajectories, manage their associated data, and further detect and resolve the conflicts (CD&R) that should eventually occur. In this research, we develop a CD&R framework for managing predicted 4-Dimensional Trajectory (4DT). Using Not Only SQL (NoSQL) database (Cassandra and MongoDB), the 4D trajectories of related routes are presented, and the possible conflicts are detected using the strategy of Computing in NoSQL Database. Compared with other conflict detection algorithms, usually by the pairwise method with O(n2) at least, the proposed Decision Tree Pruning Method (DTPM) effectively treats massive data sets. The 4DT data are collected by Trajectory Predictor (TP) concerning 58% of the whole Brazilian air traffic. The comparison results between Cassandra and MongoDB from the case studies show the effectiveness of the proposed methods for conflict detection. In addition, we prove that the conflict resolution approach is viable for application in real scenarios, finding near-optimal solutions for the conflicts identified by the framework. Finally, we also demonstrated the development of sustainable artificial intelligence in intelligent air transportation to improve safety in air traffic management.


Download data is not yet available.

Author Biographies

Lucas Borges Monteiro, Universidade de BrasiliaUniversity of Brasilia (UnB), Department of Computer Science (CIC)

Lucas Borges Monteiro is a Ph.D candidate at the Computer Science Department (CiC) at University of Brasılia (UnB), Brazil and a Computer Engineer graduated from the Federal University of Goiás (UFG). He received the M.Sc. degree in informatics from the UnB. He currently works at Brazilian Internal Revenue Service (IRS). His research interests are artificial intelligence, machine learning, and air traffic management.

Vitor Filincowsky Ribeiro, University of Brasilia (UnB), Department of Computer Science (CIC)

Vitor Filincowsky Ribeiro received the graduate degree in computer science, the M.Sc. degree and PhD in informatics from the University of Brasília (UnB), Brazil. He is a Senior Systems Analyst with the Brazilian Society for IT Development in Brasília. He graduated with a thesis on collaborative decision making. His current research interests include cooperative algorithms for resource allocation and scenario forecast with the emphasis in ATFM. He was a recipient of the Brazilian National Confederation of Transportation Prize in 2013 and also Honorable Mention Thesis of the UnB Dissertation and Thesis Award 2018 and 2019.

Cristiano Perez Garcia, University of Brasilia (UnB), Department of Computer Science (CIC)

Cristiano P. Garcia is a M.Sc. Student at the Computer Science Department (CiC) at University of Brasılia (UnB). He received his Bachelor’s degree in Computer Science from the University of São Paulo (USP) in 2009. He is also a Brazilian Air Force Captain specializing in Air Traffic Management. He currently works at CINDACTA I, a Regional Unit of the Department of Airspace Control (DECEA). His research interests are artificial intelligence, machine learning, air traffic management, and urban air mobility.

Geraldo Pereira Rocha Filho, University of Brasilia (UnB), Department of Computer Science (CIC)

Geraldo P. Rocha Filho is an Assistant Professor at the Computer Science Department (CiC) at University of Brasılia (UnB). He received his Ph.D. in Computer Science from the University of São Paulo (USP) in 2018. He received his M.Sc. from the USP in 2014. He was also a post-doctoral research fellow at the Institute of Computing at UNICAMP before joining the UnB. His research interests are wireless sensor networks, vehicular networks, smart grids, smart home and machine learning.

Li Weigang, University of Brasilia (UnB), Department of Computer Science (CIC)

Li Weigang is a full professor and coordinator of TransLab of the Department of Computer Science at the University of Brasilia (UnB), Brazil. In 1994, he received Ph.D. degree from the Aeronautics Institute of Technology (ITA), Brazil. Currently, he is a fellow researcher (PQ) from Brazilian National Council for Scientific and Technological Development (CNPq) and has coordinated various R&D projects supported by some promotion foundations and also the industry partners. He advised more than 100 students including PhD students and Postdoctoral researchers during his long career. His main research interest is Artificial Intelligence and its applications such as Intelligent Air Transportation and Machine Learning.


J. E. Dieudonne, H. L. Crane, S. R. Jones, C. J. Smith, S. A. Remillard, and G. Snead, “Neo (nextgen 4d tm) provided by swim’s surveillance soa (sdn asp for rnp 4d ops),” in 2007 Integrated Communications, Navigation and Surveillance Conference, pp. 1–12, April 2007.

A. Rodríguez-Sanz, D. Álvarez, F. Comendador, R. Valdés, J. Pérez- Castán, and M. Godoy, “Air traffic management based on 4d trajectories: A reliability analysis using multi-state systems theory,” Transportation Research Procedia, vol. 33, pp. 355 – 362, 2018. XIII Conference on Transport Engineering, CIT2018.

G. Frontera, J. Besada, A. Bernardos, E. Casado, and J. López-Leonés, “Formal intent-based trajectory description languages,” IEEE Transac- tions on Intelligent Transportation Systems, vol. 15, pp. 1550–1566, Aug 2014.

M. Vilaplana, E. Gallo, and F. Navarro, “Towards a formal language for the common description of aircraft intent,” 24th Digital Avionics Systems Conference, vol. 1, pp. 3.C.5–3.1–9, 2005.

J. K. Kuchar and L. C. Yang, “A review of conflict detection and resolution modeling methods,” IEEE Transactions on Intelligent Trans- portation Systems, vol. 1, no. 4, pp. 179–189, 2000.

Y.-J. Chiang, J. T. Klosowski, C. Lee, and J. S. Mitchell, “Geometric algorithms for conflict detection/resolution in air traffic management,” in Proceedings of the 36th IEEE Conference on Decision and Control, vol. 2, pp. 1835–1840, IEEE, 1997.

Z. Wang, M. Liang, D. Delahaye, and W. Wu, “Learning real trajectory data to enhance conflict detection accuracy in closest point of approach,” in USA/Europe Air Traffic Management Research and Development Seminar (ATM2019), pp. 1–8, 2019.

R. A. Paielli and H. Erzberger, “Trajectory specification for terminal airspace: Conflict detection and resolution,” Journal of Air Transporta- tion, vol. 27, no. 2, pp. 51–60, 2019.

J.Tang,“Conflictdetectionandresolutionforcivilaviation:Aliterature survey,” IEEE Aerospace and Electronic Systems Magazine, vol. 34, no. 10, pp. 20–35, 2019.

S.Ruiz,M.Piera,J.Nosedal,andA.Ranieri,“Strategicde-conflictionin the presence of a large number of 4d trajectories using a causal modeling approach,” Transportation Research Part C: Emerging Technologies, vol. 39, pp. 39:129–147, 2014.

S. Ayhan, P. Costas, and H. Samet, “Prescriptive analytics system for long-range aircraft conflict detection and resolution,” in 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 239 – 248, Nov 2018.

G. G. N. Sandamali, R. Su, K. L. K. Sudheera, Y. Zhang, and Y. Zhang, “Two-stage scalable air traffic flow management model under uncertainty,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 12, pp. 7328–7340, 2021.

R. Sabatini, A. Gardi, K. D. Ridder, S. Ramasamy, and L. Salazar, “Automated intent negotiation and validation system for 4-dimensional trajectory based operations,” in European Navigation Conference (ENC), p. pp, 04 2013.

K.quanCai,Y.wuTang,andW.Wang,“Anevolutionarymulti-objective approach for network-wide conflict-free flight trajectories planning,” in 2015 IEEE/AIAA 34th Digital Avionics Systems Conference (DASC), pp. 1D2–1–1D2–10, Sep 2015.

J. Rosenow, M. Lindner, and J. Scheiderer, “Advanced flight planning and the benefit of in-flight aircraft trajectory optimization,” Sustainabil- ity, vol. 13, no. 3, 2021.

I.R.DeOliveira,V.F.Ribeiro,L.Weigang,andR.C.Garcia,“Detecting violation of aircraft separation requirements,” Feb. 4 2020. US Patent 10,553,121.

J. Besada, G. Frontera, J. Crespo, E. Casado, and J. López-Leonés, “Automated aircraft trajectory prediction based on formal intent-related language processing,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, Sep 2013.

EUROCONTROLandFAA,“Commontrajectorypredictorstructureand terminology,” Tech. Rep. Version 1.0, Action Plan 16 White Paper, Jan 29th 2010.

M. O. Ball, R. L. Hoffman, D. Knorr, J. Wetherly, and M. Wambsganss, “Assessing the benefits of collaborative decision making in air traffic management,” Progress in Astronautics and Aeronautics, vol. 193, pp. 239–252, 2001.

J. López-Leonés, M. A. Vilaplana, E. Gallo, F. A. Navarro, and C. Querejeta, “The aircraft intent description language: A key enabler for air-ground synchronization in trajectory-based operations,” in 2007 IEEE/AIAA 26th Digital Avionics Systems Conference, pp. 1.D.4–1– 1.D.4–12, Oct 2007.

R. E. Korf, Artificial Intelligence Search Algorithms, p. 22. Chapman amp; Hall/CRC, 2 ed., 2010.

DECEA, “Anuário estatístico de tráfego aéreo,” tech. rep., Headquarters of Air Navigation Management (CGNA), 2018.



How to Cite

Monteiro, L. B., Ribeiro, V. F., Garcia, C. P., Rocha Filho, G. P., & Weigang, L. (2022). 4D Trajectory Conflict Detection and Resolution Using Decision Tree Pruning Method. IEEE Latin America Transactions, 21(2), 277–287. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6811