Citation

Mithra, Krishnamoorthy Sathya and Karthikeyan, Ganesh and Nagarajan, Deivanayagampillai and Vellaichamy, Parthasarathy (2026) Enhanced air traffic prediction using XGBoost. In: Data-driven Decision Making and Soft Computing. CRC Press, pp. 193-206. ISBN 978-100363483-6, 978-104106297-4, 978-104106314-8

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Abstract

Air traffic forecasting is necessary to enhance airport operations and aviation safety. This chapter presents a new XGBoost-based forecasting model, which uses properly prepared and feature-engineered data to enhance forecasting. The XGBoost model, based on the methods of boosting, enhances prediction by learning from low-value neurons, boosting performance. The data includes significant features such as year, total arrivals and departures, and Instrument Flight Rules (IFR) operating flights. The proposed model assists in effective air traffic control (ATC), runway allocation, and passenger flow optimization, ensuring smooth airport operations. The findings assist airports and airlines in taking informed decisions, resulting in enhanced operational efficiency and safety measures. With air traffic expanding internationally, the ability to predict and manage aircraft movement efficiently will be critical to ensuring airport performance and regulation compliance.

Item Type:	Book Section
Uncontrolled Keywords:	Air navigation, Air traffic control, Air transportation
Subjects:	T Technology > TJ Mechanical Engineering and Machinery > TJ751-805 Miscellaneous motors and engines Including gas, gasoline, diesel engines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Sep 2026 07:13
Last Modified:	03 Sep 2026 07:13
URII:	http://shdl.mmu.edu.my/id/eprint/16636

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