Prediction of Flight Areas using Machine Learning Algorithm-Reference-Cited by-同舟云学术

Prediction of Flight Areas using Machine Learning Algorithm

Published:2024-08-21 Issue: Volume:2 Page:
ISSN:3046-403X
Container-title:LatIA
language:
Short-container-title:LatIA

Author:

Singh Khushwant^ORCID,Yadav Mohit^ORCID,Hacimahmud Abdullayev Vugar^ORCID

Abstract

Anyone who often uses the airways wants to predict when it will be best to purchase a ticket in order to get the best possible value. Aircraft firms continuously adjust ticket prices in an effort to maximize profits. When it's anticipated that demand for more income will grow, aircraft manufacturers may raise flying prices. Information analysis for a given air route, comprising the features like take-off time, entrance time, and airways during a specified period, has been gathered in order to decrease costs. To use the machine learning models, qualities are arranged based on the information that has been gathered. The machine learning approach to determine costs based on attributes is presented in the paper below.

Publisher

A & G Editor

Reference37 articles.

1. Stefanovič, P., Štrimaitis, R., & Kurasova, O. (2020). Prediction of flight time deviation for lithuanian airports using supervised machine learning model. Computational Intelligence and Neuroscience, 2020(1), 8878681.

2. Truong, D., & Choi, W. (2020). Using machine learning algorithms to predict the risk of small Unmanned Aircraft System violations in the National Airspace System. Journal of Air Transport Management, 86, 101822.

3. Gui, G., Liu, F., Sun, J., Yang, J., Zhou, Z., & Zhao, D. (2019). Flight delay prediction based on aviation big data and machine learning. IEEE Transactions on Vehicular Technology, 69(1), 140-150.

4. Kim, J., Justin, C., Mavris, D., & Briceno, S. (2022). Data-driven approach using machine learning for real-time flight path optimization. Journal of Aerospace Information Systems, 19(1), 3-21.

5. Esmaeilzadeh, E., & Mokhtarimousavi, S. (2020). Machine learning approach for flight departure delay prediction and analysis. Transportation Research Record, 2674(8), 145-159.