Flight Load Factor Predictions based on Analysis of Ticket Prices and other Factors
The ability to forecast traffic and to size the operation accordingly is a determining factor, for airports. However, to realise its full potential, it needs to be considered as part of a holistic approach, closely linked to airport planning and operations. To ensure airport resources are used efficiently, accurate information about passenger numbers and their effects on the operation is essential. Therefore, this study explores machine learning capabilities enabling predictions of aircraft load factors.
Dynamic Pairwise Wake Vortex Separations For Arrivals Using Predictive Machine Learning Models
Aircraft wake behaviour and meteorological information is monitored and processed using ML algorithms which determine the wake separation minimum reductions that can be safely applied between subsequent arriving aircraft.
Machine Learning Supporting Enhanced Optimized Spacing Delivery between Consecutive Departing Aircraft
This paper introduces the enhanced Optimised Spacing Delivery tool which builds on the OSD tool using Machine Learning to make more accurate predictions of aircraft behaviour and wind on the initial departure path.
Applying Machine Learning Modeling to Enhance Runway Throughput at A Big European Airport
The present paper shows how a Machine Learning (ML) analysis can support the development of accurate, yet operational, models for runway occupancy time prediction depending on all impact parameters.
Data Mining and ML Techniques Supporting TBS Concept Deployment
The paper presents two methods to allow air traffic controllers to deliver separation minima accurately and safely, on the basis of time intervals instead of distances.
Air traffic control agency enables up to 4 more aircraft landings per hour
AI models predict aircraft behavior and optimize capacity, enabling safer, more accurate separation and more efficient runway and flight‑path management.