Machine learning-enhanced aircraft landing scheduling under uncertainties

Abstract

Aircraft delays lead to safety concerns and financial losses, which can propagate for several hours during extreme scenarios. Developing an efficient landing scheduling method is one of the effective approaches to reducing flight delays and safety concerns. Existing scheduling practices are mostly done by air traffic controllers (ATC) with heuristic rules. This paper proposes a novel machine learning-enhanced methodology for aircraft landing scheduling. Data-driven machine learning (ML) models are proposed to enhance automation and safety. ML enhancement is adopted for both prediction and optimization. First, the flight arrival delay scenarios are analyzed to identify the delay-related factors, where strong multimodal distributions and arrival flight time duration clusters are observed. A multi-stage conditional ML predictor is proposed for improved prediction performance of separation time conditioned on flight events. Next, we propose incorporating the ML predictions as safety constraints of the time-constrained traveling salesman problem formulation. The scheduling problem is then solved with mixed-integer linear programming (MILP). Additionally, uncertainties between successive flights from historical flight recordings and model predictions are included to ensure reliability. We demonstrate the real-world applicability of our method using the flight track and event data from the Sherlock database of the Atlanta Air Route Traffic Control Center (ARTCC ZTL). The case studies provide evidence that the proposed method is capable of reducing the total landing time by an average of 17.2% across three case studies, when compared to the First-Come-First-Served (FCFS) rule. Unlike the deterministic heuristic FCFS rule, the proposed methodology also considers the uncertainties between aircraft and ensures confidence in the scheduling. Finally, several concluding remarks and future research directions are given. The code used can be retrieved from Link.