Multi-Flight Rerouting Optimisation Based on Typical Flight Paths Under Convective Weather in the Terminal Area
With the rapid growth of flight volume, the impact of convective weather on flight operations in the terminal area has become more and more serious. In this paper, the typical flight paths (TFPs) are used to replace flight procedures as the routine flight paths in the terminal area, and the TFP of each flight is predicted by Random Forest (RF), Boosting Tree (BT) and K-Nearest Neigh-bour (KNN) algorithms based on the weather and flight plan characteristics. A multi-flight rerouting optimisa-tion model by bi-level programming is established, which contains a flight flow optimisation model in the upper layer and a single flight path optimisation model in the lower layer. The simulated annealing algorithm and the bidirectional A* algorithm are used to solve the upper and lower models. This paper uses the terminal area of Guangzhou Baiyun Airport (ZGGG) and Wuhan Tianhe Airport (ZHHH) for case analysis. The RF algorithm has better performance in predicting TFPs compared with the BT and KNN algorithms. Compared to the historical radar trajectory, the flight path optimisation results show that for the Guangzhou terminal area, while meeting the Terminal Airspace Availability (TAA) as constraint, the flight flow increases and the flight distance reduces, ef-fectively improving the operational efficiency within the terminal.
Department of Development Planning. Statistical bulletin on the development of the civil aviation industry from 2017-2021. Civil Aviation Administration of China; 2018-2022.
Civil Aviation Administration of China. Civil aviation air traffic management rules. Civil Aviation Administration of China. Report number: 30, 2017.
Campbell S, Matthews M, Delaura R. Air traffic decision analysis during convective weather events in arrival airspace. AIAA. 2012. doi: 10.2514/6.2012-5502.
Lin YH, Balakrishnan H. Prediction of terminal-area weather penetration on the basis of operational factors. Transportation Research Board. 2014;2400(1): 45-53. doi: 10.3141/2400-06.
Li J, et al. Convective weather avoidance prediction in en route airspace based on support vector machine. Transactions of Nanjing University of Aeronautics and Astronautics. 2021;38(4): 656-670. doi: 10.3390/aerospace9040189.
Sridhar B, Chatterji, G, Grabbe, S, Sheth, K. Integration of traffic flow management decisions. AIAA. 2013. doi: 10.2514/6.2002-5014.
Dwight W, Arthur, W, Heagy, W, Kirk, D. Assessment of prediction error impact on resolutions for aircraft and severe weather avoidance. AIAA. 2006. doi: 10.2514/6.2004-6265.
Isaacson D, et al. Laboratory evaluation of dynamic routing of air traffic in an en route arrival metering environment. AIAA. 2018. doi: 10.2514/6.2018-3985.
Wang L, Wang X, Wei F. Mobile and algorithm of rerouting under adverse weather with moving and shrinking. Journal of Henan University of Science and Technology (Natural Science). 2017;38(03): 35-40. doi: 10.15926/j.cnki.issn1672-6871.2017.03.008.
Wang F, Wang H. A re-routing path planning method based on Maklink Graph and GA algorithm. Journal of Transportation Systems Engineering and Information Technology. 2014;14(5): 154-160. doi: 10.16097/j.cnki.1009-6744.2014.05.058.
Soler M, Zou B, Hansen M. Flight trajectory design in the presence of contrails: Application of a multiphase mixed-integer optimal control approach. Transportation Research Part C: Emerging Technologies. 2014;48: 172-194. doi: 10.1016/j.trc.2014.08.009.
Zhang Z, Wei Z. A dynamic deviation method for terminal control areas under scattered hazardous weather. China Safety Science Journal. 2016;26(1): 40-44. doi: 10.16265/j.cnki.issn1003-3033.2016.01.007.
Taylor C, et al. Generating diverse reroutes for tactical constraint avoidance. Air Traffic Control Quarterly. 2018;26(2): 49-59. doi: 10.2514/1.D0089.
Taylor C, Wanke C. Improved dynamic generation of operationally acceptable reroutes using network optimisation. Journal of Guidance, Control, and Dynamics. 2011;34(4): 961-975. doi: 10.2514/1.52957.
Xie Z, Zhong Z. Aircraft path planning under adverse weather conditions. Matec Web of Conferences. 2016;77:15001. doi: 10.1051/matecconf/20167715001.
Mao L, Peng Y, Jia Z. Tactical rerouting in severe weather. Journal of East China Jiaotong University. 2020;37(02): 72-80. doi: 10.16749/j.cnki.jecjtu.2020.02.010.
Ayo BS. An improved genetic algorithm for flight path re-routes with reduced passenger impact. Journal of Computer and Communications. 2017;05(07): 65-76. doi: 10.4236/jcc.2017.57007.
Cai K, Tang Y, Wei W. An evolutionary multi-objective approach for network-wide conflict-free flight trajectories planning. IEEE/AIAA Digital Avionics Systems Conference (DASC). 2015;1D2-1-1D2-10. doi: 10.1109/DASC.2015.7311345.
Taylor C, Wanke C. Generating operationally-acceptable reroutes using simulated annealing. AIAA. 2010; 9017. doi: 10.2514/6.2010-9017.
Chaimatanan S, Delahaye D, Mongeau M. A methodology for strategic planning of aircraft trajectories using simulated annealing. ISIATM, International Conference on Interdisciplinary Science for Air traffic Management. 2012. doi: hal-00912772.
Eduardo A, et al. Informed scenario-based RRT* for aircraft trajectory planning under ensemble forecasting of thunderstorms. Transportation Research Part C: Emerging Technologies. 2021;129: 103232. doi: 10.1016/j.trc.2021.103232.
Liu, Y, Hansen, M. Predicting aircraft trajectories: A deep generative convolutional recurrent neural networks approach. ArXiv Preprint ArXiv:1812.11670. 2018. doi: 10.48550/arXiv.1812.11670.
Ayhan S, Samet H. Aircraft trajectory prediction made easy with predictive analytics. Association for Computing Machinery. 2016; 21-30. doi: 10.1145/2939672.2939694.
Pang Y, Liu Y. Conditional generative adversarial networks (CGAN) for aircraft trajectory prediction considering weather effects. AIAA. 2020; 1853. doi: 10.2514/6.2020-1853.
Ankerst M, et al. OPTICS: Ordering points to identify the clustering structure. Association for Computing Machinery. 1999;28(2): 49-60. doi: 10.1145/304181.304187.
Krozel J, et al. Capacity estimation for airspaces with convective weather constraints. AIAA. 2007; 6451. doi: 10.2514/6.2007-6451.
Rubnich M, Delaura R. An algorithm to identify robust convective weather avoidance polygons in en route airspace. AIAA. 2010; 9164. doi: 10.2514/6.2010-9164.
Matthews M, Delaura R, Venuti J. Strategic forecasts of TRACON airspace capacity during convective weather impacts. AIAA. 2017; 3430. doi: 10.2514/6.2017-3430.
Copyright (c) 2022 Shijin Wang, Rongrong Duan, Jiewen Chu, Jiahao Li; Baotian Yang
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).