Departure flight delay prediction due to ground delay program using Multilayer Perceptron with improved sparrow search algorithm

Abstract

The ground delay program (GDP) is a commonly used tool in air traffic management. Developing a departure flight delay prediction model based on GDP can aid airlines and control authorities in better flight planning and adjusting air traffic control strategies. A model that combines the improved sparrow search algorithm (ISSA) and Multilayer Perceptron (MLP) has been proposed to minimise prediction errors. The ISSA uses tent chaotic mapping, dynamic adaptive weights, and Levy flight strategy to enhance the algorithm’s accuracy for the sparrow search algorithm (SSA). The MLP model’s hyperparameters are optimised using the ISSA to improve the model’s prediction accuracy and generalisation performance. Experiments were performed using actual GDP-generated departure flight delay data and compared with other machine learning techniques and optimisation algorithms. The results of the experiments show that the mean absolute error (MAE) and root mean square error (RMSE) of the ISSA-MLP model are 16.8 and 24.2, respectively. These values are 5.61%, 6.3% and 1.8% higher in MAE and 4.4%, 5.1% and 2.5% higher in RMSE compared to SSA, particle swarm optimisation (PSO) and grey wolf optimisation (GWO). The ISSA-MLP model has been verified to have good predictive and practical value.

The Analysis and AI Simulation of Passenger Flows in an Airport Terminal: A Decision-Making Tool

“…In terms of operational efficiency, Thampan et al [14] have presented and analyzed how analytical methods can be used to evaluate service quality, considering the spatial, operational and technological changes in airport terminals. More recently, Dong et al [15] have used machine learning techniques to predict incidents leading to flight delays. This predictive capability could improve flight scheduling for more effective passenger service and mitigate security and economic risks in the airport environment.…”