An Airfield Area Layout Efficiency Analysis Method Based on Queuing Network and Machine Learning

Abstract

The layout design of an airfield area plays a crucial role in ensuring the efficiency of aircraft ground operations. In order to minimize delays caused by insufficient capacity and prevent resource wastage due to excessive capacity during the operational phase, this paper developed a prediction model for operational efficiency leveraging queuing network theory and machine-learning models. Our approach involves four key steps: (1) establish a theoretical framework for analyzing the operational efficiency of airfield area layouts based on queuing network theory, (2) employ a combination of discrete modeling and multi-agent modeling to construct a simulation model for ground operations in the airfield area, (3) develop a prediction model, known as PSO-ANN, for forecasting the operational efficiency of the airfield area using the simulation results, (4) conduct computer-based simulation experiments to assess the sensitivity of airfield area parameters, observe traffic-flow phase transitions, and investigate the factors influencing operational efficiency. This methodology enables the rapid assessment of operational efficiency for small- and medium-sized airports, as well as regional multi-airport systems. It is particularly useful for program evaluation during the strategic planning phase.