Research on Check-In Baggage Flow Prediction for Airport Departure Passengers Based on Improved PSO-BP Neural Network Combination Model

Accurate forecasting of passenger checked baggage traffic is crucial for efficient and intelligent allocation and optimization of airport service resources. A systematic analysis of the influencing factors and prediction algorithms for the baggage flow is rarely included in existing studies. To accurately capture the trend of baggage flow, a combined PCC-PCA-PSO-BP baggage flow prediction model is proposed. This study applies the model to predict the departing passengers’ checked baggage flow at Chengdu Shuangliu International Airport in China. First, in the preprocessing of the data, multiple interpolation demonstrates a better numerical interpolation effect compared to mean interpolation, regression interpolation, and expectation maximization (EM) interpolation in cases of missing data. Second, in terms of the influencing factors, unlike factors that affect the airport passenger flow, the total retail sales of consumer goods have a weak relationship with the baggage flow. The departure passenger flow and flight takeoff and landing sorties play a dominant role in the baggage flow. The railway passenger flow, highway passenger flow, and months have statistically significant effects on the changes in the baggage flow. Factors such as holidays and weekends also contribute to the baggage flow alternation. Finally, the PCC-PCA-PSO-BP model is proposed for predicting the baggage flow. This model exhibits superior performance in terms of the network convergence speed and prediction accuracy compared to four other models: BP, PCA-BP, PSO-BP, and PCA-PSO-BP. This study provides a novel approach for predicting the flow of checked baggage for airport departure passengers.