Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties

2023/11/27 by Pang, Yutian, Zhao, Peng, Hu, Jueming +1
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)

paper · doi:10.48550/arxiv.2311.16030

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.

Related

• Flight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of Major Airlines in the United States
• Deciphering Air Travel Disruptions: A Machine Learning Approach
• Queue up for takeoff: a transferable deep learning framework for flight delay prediction
• A Data Mining Approach to Flight Arrival Delay Prediction for American Airlines
• A Graph-Enhanced Deep-Reinforcement Learning Framework for the Aircraft Landing Problem