Developing Novel Features Employed by Regression Methods to Solve Air Traffic Delay Prediction Problems
Received: 21 January 2026 | Revised: 21 February 2026 | Accepted: 28 February 2026 | Online: 4 April 2026
Corresponding author: Linh Tran
Abstract
Flight delays are influenced not only by flight-specific factors but also by network effects, where disruptions at central airports propagate through downstream connections. This study proposes a lightweight, model-agnostic framework that injects network structure into standard flight-level regression for arrival delay prediction. Using Bureau of Transportation Statistics (BTS) on-time performance data (523,435 completed flights from 2025-01-01 to 2025-01-31), the study builds (i) a directed airport-route graph weighted by observed origin–destination counts and (ii) a rotation hypergraph in which each (tail number, day) forms a hyperedge connecting all airports visited, weighted by the number of legs. From these structures, four airport-level ranking signals are derived: Graph PageRank (GPR), GCN-Style Fixed-Point Ranking (GGCN), Hypergraph PageRank (HPR), and Hypergraph Fixed-Point Ranking (HGCN). Origin and destination ranking features are attached to each flight, and Linear Regression (LR), K-Nearest Neighbors (KNN) regression, and Multi-Layer Perceptron (MLP) regression models are evaluated under a time-aware split. Network-enriched features yield modest but consistent gains, with the best performance achieved by MLP using all ranking features.
Keywords:
feature engineer, graph theory, PageRank, neural network, regressionDownloads
References
"Airline On-Time Performance Data (TranStats Database)." U.S. Department of Transportation, Bureau of Transportation Statistics, 2025, [Online]. Available: https://www.transtats.bts.gov/ontime/.
J. Sun. et al, "Designing Recurrent and Graph Neural Networks to Predict Airport and Air Traffic Network Delays," in 10th International Conference for Research in Air Transportation FAA & Eurocontrol, Tampa, FL, USA, Jun. 2020, pp. 1–8.
X. Shen, J. Chen, and R. Yan, "A Spatial–Temporal Model for Network-wide Flight Delay Prediction Based on Federated Learning," Applied Soft Computing, vol. 154, Mar. 2024, Art. no. 111380. DOI: https://doi.org/10.1016/j.asoc.2024.111380
S. Brin and L. Page, "The Anatomy of a Large-Scale Hypertextual Web Search Engine," Computer Networks and ISDN Systems, vol. 30, no. 1–7, pp. 107–117, Apr. 1998. DOI: https://doi.org/10.1016/S0169-7552(98)00110-X
J. L. Franco, M. V. M. Neto, F. A. N. Verri, and D. R. Amancio, "Graph Machine Learning for Flight Delay Prediction Due to Holding Maneuver," Physica A: Statistical Mechanics and its Applications, vol. 685, Mar. 2026, Art. no. 131318. DOI: https://doi.org/10.1016/j.physa.2026.131318
A. N. Langville and C. D. Meyer, Eds., Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton, NJ, USA: Princeton University Press, 2012. DOI: https://doi.org/10.2307/j.ctt7t8z9
T. N. Kipf and M. Welling, "Semi-Supervised Classification with Graph Convolutional Networks," in 5th International Conference on Learning Representations, Toulon, France, Apr. 2017.
S. Zhang, H. Tong, J. Xu, and R. Maciejewski, "Graph Convolutional Networks: A Comprehensive Review," Computational Social Networks, vol. 6, no. 1, Dec. 2019, Art. no. 11. DOI: https://doi.org/10.1186/s40649-019-0069-y
R. Li, S. Wang, F. Zhu, and J. Huang, "Adaptive Graph Convolutional Neural Networks," in Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, LA, USA, Feb. 2018, pp. 3546–3553. DOI: https://doi.org/10.1609/aaai.v32i1.11691
P. E. Pope, S. Kolouri, M. Rostami, C. E. Martin, and H. Hoffmann, "Explainability Methods for Graph Convolutional Neural Networks," in IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, Jun. 2019, pp. 10764–10773. DOI: https://doi.org/10.1109/CVPR.2019.01103
D. Zhou, J. Huang, and B. Schölkopf, "Learning with Hypergraphs: Clustering, Classification, and Embedding," in Advances in Neural Information Processing Systems 19, B. Schölkopf, J. Platt, and T. Hofmann, Eds. Cambridge, MA, USA: The MIT Press, 2007, pp. 1601–1608. DOI: https://doi.org/10.7551/mitpress/7503.003.0205
D. Zhou, J. Huang, and B. Schölkopf, "Beyond Pairwise Classification and Clustering Using Hypergraphs," Max Planck Institute for Biological Cybernetics, Tübingen, Germany, Technical Report TR-143, Aug. 2005.
Y. Feng, H. You, Z. Zhang, R. Ji, and Y. Gao, "Hypergraph Neural Networks," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 3558–3565, Jul. 2019. DOI: https://doi.org/10.1609/aaai.v33i01.33013558
Y. Gao, Y. Feng, S. Ji, and R. Ji, "HGNN+: General Hypergraph Neural Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 3181–3199, Mar. 2023. DOI: https://doi.org/10.1109/TPAMI.2022.3182052
J. Jiang, Y. Wei, Y. Feng, J. Cao, and Y. Gao, "Dynamic Hypergraph Neural Networks," in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Macao, China, Aug. 2019, pp. 2635–2641. DOI: https://doi.org/10.24963/ijcai.2019/366
S. Kim, S. Y. Lee, Y. Gao, A. Antelmi, M. Polato, and K. Shin, "A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide," in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, Aug. 2024, pp. 6534–6544. DOI: https://doi.org/10.1145/3637528.3671457
D. Cai, M. Song, C. Sun, B. Zhang, S. Hong, and H. Li, "Hypergraph Structure Learning for Hypergraph Neural Networks," in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, Austria, Jul. 2022, pp. 1923–1929. DOI: https://doi.org/10.24963/ijcai.2022/267
Y. Takai, A. Miyauchi, M. Ikeda, and Y. Yoshida, "Hypergraph Clustering Based on PageRank," in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2020, vol. Virtual Event, pp. 1970–1978. DOI: https://doi.org/10.1145/3394486.3403248
K. Ameranis, A. DePavia, L. Orecchia, and E. Tani, "Fast Algorithms for Hypergraph PageRank with Applications to Semi-Supervised Learning," in Proceedings of Machine Learning Research, Vienna, Austria, Jul. 2024, vol. 235, pp. 1306–1330.
L. Tran, T. Quan, and A. Mai, "PageRank algorithm for Directed Hypergraph." arXiv, 2019. DOI: https://doi.org/10.26782/jmcms.spl.4/2019.11.00004
Y. H. Piao, Y. J. Wang, and K. J. Li, "Identifying important nodes of hypergraph: An improved PageRank algorithm," Chinese Physics B, vol. 34, no. 4, Apr. 2025, Art. no. 048902. DOI: https://doi.org/10.1088/1674-1056/adb269
Downloads
How to Cite
License
Copyright (c) 2026 Loc Tran, Luong Minh Tri Nguyen, Huu Hiep Nguyen, Tan Phuc Tran, Kim Anh Phan, Linh Tran
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the 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) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
