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Modeling sector route segment capacity under convective weather based on machine learning

Published online by Cambridge University Press:  30 September 2024

S.J. Wang ,
Y.L. Li ,
B.T. Yang Open the ORCID record for B.T. Yang [Opens in a new window]  and
R.R. Duan
S.J. Wang
Affiliation:
Next Generation Intelligent Air Traffic Control Laboratory, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
Y.L. Li
Affiliation:
Next Generation Intelligent Air Traffic Control Laboratory, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
B.T. Yang*
Affiliation:
Next Generation Intelligent Air Traffic Control Laboratory, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
R.R. Duan
Affiliation:
Next Generation Intelligent Air Traffic Control Laboratory, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
*
Corresponding author: B. Yang; Email: 2563765531@qq.com
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Abstract

Estimating airspace capacity under convective weather conditions is crucial for ensuring the efficiency and safety of air traffic operations. Sector route segments, which are essential components of flight routes, require timely capacity predictions during operationally critical periods. In this paper, initially, an enhanced Recursive Feature Elimination algorithm is used to select meteorological data and develop predictive features. Subsequently, the CWSRC model is established using the RF supervised learning algorithm. Finally, the paper takes ENH-YIH segment as an example to predict the capacity. Compared with other machine learning algorithms, the residual percentages for KNN, MLP and RF are 86.03%, 77.37% and 93.40%, respectively, within the range of [−0.2, 0.2]. In three separate day cases, results show that the CWSRC model’s MAE, MSE, RMSE and R2 significantly outperform traditional methods like Maxflow/Mincut and scanning line. The results confirm the CWSRC model’s superior predictive capabilities.

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Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1332 , February 2025 , pp. 506 - 527
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Civil Aviation Administration of China. Civil Aviation Industry Development Statistics Bulletin for 2021 (Vol. 451). Department of Development Planning, Beijing, China, 2022, https://www.mot.gov.cn/tongjishuju/minhang/202206/P020220607377281705999.pdf.Google Scholar
FAA. Air Traffic by the Numbers, Federal Aviation Administration, Washington, 2022.Google Scholar
Rich DeLaura, M. An exploratory study of modeling en route pilot convective storm flight deviation behavior, In 12th Conference on Aviation Range and Aerospace Meteorology, 2006.Google Scholar
DeLaura, R., Robinson, M., Pawlak, M. and Evans, J. Modeling convective weather avoidance in enroute airspace. In 13th Conference on Aviation, Range, and Aerospace Meteorology, AMS, New Orleans, LA, 2008, https://ci.nii.ac.jp/naid/20000753365 Google Scholar
Klein, A., Cook, L. and Wood, B. Airspace availability estimation for traffic flow management using the scanning method, In 2008 IEEE/AIAA 27th Digital Avionics Systems Conference, 2008, pp 3-B, https://doi.org/10.1109/dasc.2008.4702802CrossRefGoogle Scholar
Song, L., Greenbaum, D. and Wanke, C. The impact of severe weather on sector capacity. In 8th USA/Europe Air Traffic Management Research and Development Seminar (ATM 2009), Napa, California, USA, 2009, pp 1–8, http://www.atmseminar.org/seminarContent/seminar8/papers/p_075_W.pdf.Google Scholar
Callaham, M., DeArmon, J., Cooper, A., Goodfriend, J., Moch-Mooney, D. and Solomos, G. Assessing NAS performance: Normalizing for the effects of weather. In 4th USA/Europe Air Traffic Management R&D Symposium, 2001, pp 3–7.Google Scholar
Klein, A., Jehlen, R. and Liang, D. Weather index with queuing component for national airspace system performance assessment, In 7th FAA/Eurocontrol ATM Seminar. Spain, 2007, http://www.atmseminar.org/seminarContent/seminar7/papers/p_024_AAPM.pdf Google Scholar
Chao, W., Xinyue, Z. and Xiaohao, X. Simulation study on airfield system capacity analysis using SIMMOD, In 2008 International Symposium on Computational Intelligence and Design, 2008, pp 87–90, https://doi.org/10.1109/iscid.2008.70 CrossRefGoogle Scholar
Sood, N. and Wieland, F. Total Airport and Airspace Model (TAAM) parallelization combining sequential and parallel algorithms for performance enhancement, In Proceedings of the 2003 Winter Simulation Conference, 2003, 2004, pp 1650–1655, https://doi.org/10.1109/wsc.2003.1261615 CrossRefGoogle Scholar
Majumdar, A., Ochieng, W.Y., McAuley, G., Lenzi, J.M. and Lepadatu, C. The factors affecting airspace capacity in Europe: A cross-sectional time-series analysis using simulated controller workload data. J. Navig., 2004, 57, (3), 385–405. https://doi.org/10.1017/s0373463304002863 CrossRefGoogle Scholar
Shi, F., Cheng, P., Geng, R. and Yang, M. An Air Traffic Flow Analysis System Using Historical Radar Data. Lecture Notes in Electrical Engineering, pp 541–549, 2012. https://doi.org/10.1007/978-3-642-25766-7_72 CrossRefGoogle Scholar
Mao, L., Peng, Y., Li, J., Guo, C., Kang, B. and Cao, Z. Random-forest based terminal capacity prediction under convective weather, System Engineering-Theory Practice, 2021, 08, pp 2125–2136.Google Scholar
Chen, J., Cai, K., Li, W., Tang, S. and Fang, J. An airspace capacity estimation model based on spatio-temporal graph convolutional networks considering weather impact, In 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), 2021, pp 1–7. https://doi.org/10.1109/dasc52595.2021.9594417 CrossRefGoogle Scholar
Wang, Y. Weather impact on airport arrival meter fix throughput. In 2017 IEEE/AIAA 36th Digital Avionics Systems Conference, 2017, pp 1–10. https://doi.org/10.1109/dasc.2017.8102133 CrossRefGoogle Scholar
Brito, I.R., Murca, M.C.R., Oliveira, M.D. and Oliveira, A.V. A machine learning-based predictive model of airspace sector occupancy, AIAA AVIATION 2021 FORUM, 2021. https://doi.org/10.2514/6.2021-2324 CrossRefGoogle Scholar
Shijin, W., Jingjing, L., Jiahao, L. and Jiewen, C. Determination of flight deviation thresholds based on the POT model. Aeronaut. Comput. Techniq., 2021, 51(3), p 5.Google Scholar
Steiner, M., Bateman, R., Megenhardt, D., Liu, Y., Xu, M., Pocernich, M. and Krozel, J. Translation of ensemble weather forecasts into probabilistic air traffic capacity impact, Air Traffic Contr. Q., 2010, 18, (3), pp 229–254. https://doi.org/10.2514/atcq.18.3.229 CrossRefGoogle Scholar
Genuer, R., Poggi, J. and Tuleau-Malot, C. Variable selection using random forests. Pattern Recognit. Lett., 2010, 31, (14), pp 2225–2236. https://doi.org/10.1016/j.patrec.2010.03.014 CrossRefGoogle Scholar
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