Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Sign In or Register Submit

PDF
PUBLISHED
30.04.2024
DOI
https://doi.org/10.7307/ptt.v36i2.349
ISSUE
2024 (Vol 36), Issue 2
LICENSE
Copyright (c) 2024 Xuanming Ren, Xinmin Tang, Kang Zhang, Qixin Lu
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Conflict Detection and Separation Configuration Method Based on Uncertain Flight Trajectory

Authors:Xuanming Ren, Xinmin Tang, Kang Zhang, Qixin Lu

Abstract

Aiming at two aircraft conflict scenario in the pre-tactical stage, by converting the uncertain flight trajectory of the target aircraft into a spatio-temporal trajectory under its performance constraints, a conflict detection model based on truncated normal distribution was proposed,
and influencing factors affecting the overall conflict probability were analysed by numerical simulation. For the conflict scenario, nonlinear  particle swarm optimisation (NPSO) algorithm was applied to solve the optimal separation configuration strategy for the ownship. The simulation results show that, in comparison to conventional PSO algorithm, the improved NPSO algorithm improves the optimal value by 14.88% and decreases the maximum velocity change by 19.84%. The simulation also shows that the algorithm can maintain the minimum interval requirements under different initial parameters, demonstrating its strong adaptability.

Keywords:conflict detection, separation configuration, spatio-temporal trajectory, uncertain flight trajectory, nonlinear particle swarm optimisation

References

  1. [1] Gomez Comendador VF, Arnaldo Valdés RM, Sanchez Cidoncha M. Impact of trajectories’ uncertainty in existing ATC complexity methodologies and metrics for DAC and FCA SESAR concepts. Energies. 2019;12(8):1-25. DOI: 10.3390/en12081559.
  2. [2] Hao SQ, Cheng SW, Zhang YP. A multi-aircraft conflict detection and resolution method for 4-dimensional trajectory-based operation. Chinese Journal of Aeronautics. 2018;7(148):177-191. DOI: 10.1016/j.cja.2018.04.017.
  3. [3] Jacquemart D, Morio J. Adaptive interacting particle system algorithm for aircraft conflict probability estimation. Aerospace Science & Technology. 2016;7(55):431-438. DOI: 10.1016/j.ast.2016.05.027.
  4. [4] Han D, et al. A conflict detection algorithm for low-altitude flights based on SVM. Journal of Beijing University of Aeronautics and Astronautics. 2018;3(44):576-582. DOI: 10.13700/j.bh.1001-5965.2017.0159.
  5. [5] Wu MG, Wang ZK, Wen XX. Aircraft conflict resolution model based on geometric optimization. Systems Engineering and Electronics. 2019;41(4):863-869. DOI: 10.3969/j.issn.1001-506X.2019.04.23.
  6. [6] Sun MH, Rand K, Fleming C. 4 Dimensional waypoint generation for conflict-free trajectory based operation. Aerospace Science and Technology. 2019;88:350-361. DOI: 10.1016/j.ast.2019.03.035.
  7. [7] Guan X, et al. A strategic flight conflict avoidance approach based on a memetic algorithm. Chinese Journal of Aeronautics. 2014;1(27):863-869. DOI: 10.1016/j.cja.2013.12.002.
  8. [8] Hernandez-Romero E, Valenzuela A, Rivas D. A probabilistic approach to measure aircraft conflict severity considering wind forecast uncertainty. Aerospace Science and Technology. 2019;86:401-414. DOI: 10.1016/j.ast.2019.01.024.
  9. [9] Russell AP, Heinz E. Conflict probability estimation generalized to non-level flight. Journal of Guidance, Control, and Dynamics. 1997;3(7):588-596. DOI: 10.2514/atcq.7.3.195.
  10. [10] Lambert A, Gruyer D, Pierre GS. A fast monte carloalgorythm for collision probability estimation. International Conference on Control, 17-20 Dec. 2008, San Antonio, USA. 2014. p. 406-411. DOI: 10.1109/ICARCV.2008.4795553.
  11. [11] Liu Y, et al. Short-term conflict detection algorithm for free flight in low-altitude airspace. Journal of Beijing University of Aeronautics and Astronautics. 2017;43(9):1873-1881. DOI: 10.13700/ j.bh.1001-5965.2016.0687.
  12. [12] Alizadeh A, Uzun M, Koyuncu E. Optimal en-route trajectory planning based on wind information. IFC. 2018;51(9):180-185. DOI: 10.1016/j.ifacol.2018.07.030.
  13. [13] Lucas BM, Vitor FR, Cristiano PG. 4D trajectory conflict detection and resolution using decision tree pruning method. IEEE Latin America Transactions. 2023;21(2):277-287. DOI: 10.1109/TLA.2023.10015220.
  14. [14] Liu ZA, Xiao G, Mao JZ. A framework for strategic online en-route operations: Integrating traffic flow and strategic conflict managements. Transportation Research Part C. 2023;147:103996. DOI: 10.1016/j.trc.2022.103996.
  15. [15] Zhang JF, Jiang HX, Wu XG. 4D trajectory prediction based on BADA and aircraft intent. Journal of Southwest Jiaotong University. 2014;49(3):1873-1881. DOI: 10.3969/j.issn.0258-2724. 2014.03.028.
  16. [16] Vito VD, Torrano G. Fast-time numerical validation of an ADS-B based automatic separation assurance and collision avoidance system. Integrated Communications Navigation and Surveillance Conference (ICNS), 20-22 Apr, Dulles, USA. 2021.
  17. [17] Ni YD, Liu P, Ma SY. An improved trajectory prediction algorithm based on ADS-B intent information. Telecommunication Engineering. 2014;43(9). DOI: 10.3969/j.issn.1001-893x. 2014.02.008.
  18. [18] Zhang K, et al. Bayesian trajectory prediction for a hypersonic gliding geentry vehicle based on intent inference. Journal of Astronautics. 2018;39(11):1258-1265. DOI: 10.3873/j.issn.1000-1328.2018.11.008.
  19. [19] Hao SQ, Zhang YP, Cheng SW. Probabilistic multi-aircraft conflict detection approach for trajectory based operation. Transportation Research: Part C. 2018;95(0):698-712. DOI: 10.1016/j.trc.2018.08.010.
  20. [20] Shi L, Wu RB, Huang XX. Conflict detection algorithm based on overall conflict probability and three-dimensional brownian motion. Journal of Electronics & Information Technology. 2015;37(2):360-366. DOI: 10.11999/JEIT140363.
  21. [21] Song Y, Miller HJ. Simulating visit probability distributions within planar space-time prisms. International Journal of Geographical Information Science. 2014;28(1):104-125. DOI: 10.1080/13658816.2013.830308.
  22. [22] Chen YT, et al. Autonomous trajectory planning and conflict management technology in restricted airspace. Acta Aeronautica et Astronautica Sinica. 2020;41(9):324045. DOI: 10.7527/S1000-6893.2020.24045.
  23. [23] Shiri H, Park J, Bennis M. Remote UAV online path planning via neural network based opportunistic control. IEEE Wireless Communication Letters. 2020;9(6):861-865. DOI: 10.1109/ LWC.2020.2973624.
  24. [24] Meng G, Fei Q. Flight conflict resolution for civil aviation based on ant colony optimization. 6th International Symposium on Computational Intelligence & Design, 28-29 Oct, Hangzhou, China. 2012.
  25. [25] Gao Y, Zhang XJ, Guan XM. Cooperative multi-aircraft conflict resolution based on co-evolution. International Symposium on Instrumentation & Measurement, 25-28 Aug, Sanya, China. 2012.
  26. [26] Shao SK, Yu P, He CL. Efficient path planning for UAV formation via comprehensively improved particle swarm optimization. ISA Transactions. 2020;97:415-430. DOI: 10.1016/j.isatra. 2019.08.018.
  27. [27] Omer J, Farges JL. Hybridization of nonlinear and mixed-integer linear programming for aircraft separation with trajectory recovery. IEEE Transactions on Intelligent Transportation Systems. 2013;14(3):1218-1230.
  28. [28] Dias F, Hijazi H, Rey D. Disjunctive linear separation conditions and mixed-integer formulations for aircraft conflict resolution. European Journal of Operational Research. 2022;296:520-538. DOI: 10.1016/j.ejor.2021.03.059.
  29. [29] Matsuno Y, et al. Stochastic optimal control for aircraft conflict resolution under wind uncertainty. ISA Transactions. 2015;43:77-88. DOI: 10.1016/j.ast.2015.02.018.
  30. [30] Emami H, Derakhshan F. Multi-agent based solution for free flight conflict detection and resolution using particle swarm optimization algorithm. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science. 2014;3(76):49-64.
  31. [31] Lu XL, He G. QPSO algorithm based on Lévy flight and its application in fuzzy portfolio. Applied Soft Computing. 2021;1(99):106894. DOI: 10.1016/j.asoc.2020.106894.
  32. [32] Lauderdale TA, et al. Separation at crossing waypoints under wind uncertainty in urban air mobility. AIAA Aviation 2021 Forum, 02 Aug, Hangzhou, China. 2021. DOI: 10.2514/6.2021-2351.
  33. [33] Yu H. A direction-constrained space-time prism-based approach for quantifying possible multi-ship collision risks. IEEE Transactions on Intelligent Transportation Systems. 2019;1(22):131-141. DOI: 10.1109/TITS.2019.2955048.
  34. [34] Malaek SM, Golachoubian M. Enhanced conflict resolution manoeuvres for dense airspaces. IEEE Transactions on Aerospace and Electronic Systems. 2019;5(56):131-141. DOI: 10.1109/TAES. 2020.2972422.
  35. [35] Han S. Industrial robot trajectory planning based on improved PSO algorithm. Journal of Physics Conference Series. 2021;1(1820):012185. DOI: 10.1088/1742-6596/1820/1/012185.
  36. [36] Wu GC. Research on path planning based on particles warm optimization. PhD thesis. Yanshan University; 2016.
Show more

SPECIAL ISSUE CALL

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
Factors Influencing Sustained Use of Shared E-Scooter Services in Urban TaiwanA Review of Expert Systems Integration in Signal Plan OptimisationCollege Students’ Cognition and Attitude Towards Connected and Autonomous Vehicles in China: an Exploratory StudyOptimising the Economic Feasibility of High-Speed Maglev Systems: A Simulation-Based Approach for Variable and Parameter Analysis

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal