Research on Prediction of Checked-baggage Departed from Airport Terminal Based on Time Series Analysis

Abstract

Checking baggage for passengers is an important part of the check-in process, which efficiency directly affects the duration when passengers stay in the airport. Predicting the amount of baggage and allocating human resources rationally can help improve the efficiency of airport operations. The amount distribution characteristics of the checked-baggage demand is analyzed based on the actual operating data of the airport in this study. A long-term prediction model based on the multiplicative seasonal model (SARIMA) is built to predict the baggage amount. Results show that the SARIMA model can describe the characteristics of time series, which has high prediction accuracy and strong robustness.