Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties

• URL: http://arxiv.org/abs/2311.16030v1
• Date: Mon, 27 Nov 2023 17:50:14 GMT
• Title: Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties
• Authors: Yutian Pang, Peng Zhao, Jueming Hu, Yongming Liu
• Abstract summary: An innovative machine learning (ML)-enhanced landing scheduling methodology is proposed, aiming to improve automation and safety. ML predictions are then integrated as safety constraints in a time-constrained traveling salesman problem formulation. Case studies demonstrate an average 17.2% reduction in total landing time compared to the First-Come-First-Served (FCFS) rule.
• Score: 14.474624795989824
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper addresses aircraft delays, emphasizing their impact on safety and financial losses. To mitigate these issues, an innovative machine learning (ML)-enhanced landing scheduling methodology is proposed, aiming to improve automation and safety. Analyzing flight arrival delay scenarios reveals strong multimodal distributions and clusters in arrival flight time durations. A multi-stage conditional ML predictor enhances separation time prediction based on flight events. ML predictions are then integrated as safety constraints in a time-constrained traveling salesman problem formulation, solved using mixed-integer linear programming (MILP). Historical flight recordings and model predictions address uncertainties between successive flights, ensuring reliability. The proposed method is validated using real-world data from the Atlanta Air Route Traffic Control Center (ARTCC ZTL). Case studies demonstrate an average 17.2% reduction in total landing time compared to the First-Come-First-Served (FCFS) rule. Unlike FCFS, the proposed methodology considers uncertainties, instilling confidence in scheduling. The study concludes with remarks and outlines future research directions.

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