Estimating Arrival and Departure Headways at Akure Airports: A Data-Driven Approach

Efficient management of arrival and departure headways is essential for optimizing runway utilization and minimizing delays at airports, particularly under high traffic conditions. This study presents a data-driven approach to estimate arrival and departure headways using predictive models and queue modelling techniques. By analyzing historical and real-time data from multiple sources, including flight operations, air traffic control, and weather conditions, we developed a model. These models dynamically estimate headway intervals based on factors like aircraft type, weather, and congestion levels. The study also incorporates queue models to simulate different operational scenarios, enabling more effective planning and runway capacity management. Key findings indicate that machine learning models can reliably predict headway intervals, allowing for real-time adjustments that balance safety and efficiency. Queue modelling further aids in understanding congestion patterns and optimizing runway allocation, reducing delays during peak periods. It also revealed that the time for length of arrival, and departure headways at Akure Airport. This research provides valuable insights into the potential of predictive and simulation-based methods for enhancing airport operational efficiency. It recommends the integration of these models into collaborative decision-making platforms, along with continuous model validation and compliance with regulatory safety standards. The approach demonstrates significant potential for airports seeking to manage capacity more dynamically and improve overall service quality.