Research on the Prediction of Flight Passenger Flow in Holidays based on Machine Learning

Abstract

Abstract This article focuses on predicting holiday flight passenger flow to optimize airline resource allocation. Analyzing daily passenger data during key holidays, including New Year’s Day, Qingming Festival, Labour Day, Dragon Boat Festival, Mid-Autumn Festival, and National Day, the study employs four machine learning models: Random Forest, Multilayer Perceptron, LightGBM, and Stacking. Findings reveal that all models effectively capture holiday flow patterns, with LightGBM demonstrating superior prediction accuracy. Moreover, creating unified models for all holidays outperforms individual holiday-specific models. The study delves into the factors influencing the varying performance of the best model across different features, providing insightful analysis and discussion.