Algorithm for Airline Error Delays Prediction to Enhanced Predictive Accuracy
DOI:
https://doi.org/10.32628/IJSRST251361Keywords:
Flight Delay Prediction, Machine Learning, Deep Learning, Denoising Autoencoder, Levenberg- Marquardt Algorithm, Airline OperationsAbstract
Flight delays have significant implications on airline efficiency and customer satisfaction. Existing prediction models often struggle with accuracy due to the complexity, volume, and noisiness of flight-related data. This study proposes an advanced predictive model using Deep Learning (DL), specifically a Stacked Denoising Autoencoder combined with the Levenberg-Marquardt (LM) algorithm (SDA-LM). The model leverages features such as flight time duration and previous flight delays. Comparative analysis with SAE-LM and SDA models using both balanced and imbalanced datasets shows the SDA-LM model achieves superior precision, accuracy, sensitivity, and F-measure. Experimental results on U.S. domestic airline datasets demonstrate that SDA-LM outperforms traditional methods including RNN in delay prediction.
📊 Article Downloads
References
X. Dong, X. Zhu, and J. Zhang, “Departure flight delay prediction due to ground delay program using Multilayer Perceptron with improved sparrow search algorithm,” The Aeronautical Journal, vol. 128, no. 1322, pp. 706–724, Sep. 2023, doi: 10.1017/aer.2023.83. DOI: https://doi.org/10.1017/aer.2023.83
J. Benesty, M. M. Sondhi, and Y. A. Huang, Springer Handbook of Speech Processing. 2007. doi: 10.1007/978-3-540-49127-9. DOI: https://doi.org/10.1007/978-3-540-49127-9
R. Abduljabbar, H. Dia, S. Liyanage, and S. A. Bagloee, “Applications of Artificial Intelligence in Transport: An Overview,” Sustainability, vol. 11, no. 1, p. 189, Jan.2019, doi: 10.3390/su11010189. DOI: https://doi.org/10.3390/su11010189
H. M. Gomes et al., “Adaptive random forests for evolving data stream classification,” Machine Learning, vol. 106, no. 9–10, pp. 1469–1495, Jun. 2017, doi: 10.1007/s10994-017-5642-8. DOI: https://doi.org/10.1007/s10994-017-5642-8
Y. R. Shrestha, S. M. Ben-Menahem, and G. Von Krogh, “Organizational Decision- Making structures in the age of artificial Intelligence,” California Management Review, vol. 61, no. 4, pp. 66–83, Jul. 2019, doi: 10.1177/0008125619862257. DOI: https://doi.org/10.1177/0008125619862257
H. Barbosa et al., “Human mobility: Models and applications,” Physics Reports, vol. 734, pp. 1–74, Feb. 2018, doi: 10.1016/j.physrep.2018.01.001. DOI: https://doi.org/10.1016/j.physrep.2018.01.001
D. Brzezinski and J. Stefanowski, “Reacting to different types of concept drift: the Accuracy Updated ensemble algorithm,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 1, pp. 81–94, Apr. 2013, doi: 10.1109/tnnls.2013.2251352. DOI: https://doi.org/10.1109/TNNLS.2013.2251352
B. Yu, Z. Guo, S. Asian, H. Wang, and G. Chen, “Flight delay prediction for commercial air transport: A deep learning approach,” Transportation Research Part E Logistics and Transportation Review, vol. 125, pp. 203–221, Mar. 2019, doi:10.1016/j.tre.2019.03.013. DOI: https://doi.org/10.1016/j.tre.2019.03.013
E. U. Weber and P. C. Stern, “Public understanding of climate change in the United States.,” American Psychologist, vol. 66, no. 4, pp. 315–328, Jan. 2011, doi: 10.1037/a0023253. DOI: https://doi.org/10.1037/a0023253
M. Siering, A. V. Deokar, and C. Janze, “Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews,” Decision Support Systems, vol. 107, pp. 52–63, Jan. 2018, doi: 10.1016/j.dss.2018.01.002. DOI: https://doi.org/10.1016/j.dss.2018.01.002
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0
