## 5 Conclusions  
The study of checked baggage flow at airports is critical to the health of the civil aviation industry. This paper thoroughly reviews the current state of the research on baggage flow demand, analyzes the factors that influence baggage flow, and investigates related models for predicting flow. Based on these findings, the paper anticipates trends in baggage flow demand research and outlines potential research directions. This analysis aims to provide a reference for the organization and management of baggage transportation, as summarized below:  
1. The prediction of checked baggage flow for departing passengers at airports exhibits characteristics of nonlinearity and strong randomness. Although there exists a significant correlation between passenger flow and baggage flow, the growth in departing passenger flow at airports does not necessarily result in a proportional increase in checked baggage flow. The relationship between these two variables is nonlinear, implying that solely relying on forecasting passenger flow to predict baggage flow may overlook the inherent characteristics associated with it.
2. When studying the relationship between the economy and baggage flow, GDP should not be used as a sole indicator in general. Instead, it should be comprehensively analyzed in conjunction with other economic indicators such as the total retail sales of consumer goods and value added of the tertiary sector.
3. How some factors that are difficult to quantify, such as passenger psychological factors, airport service levels, checked baggage prices, and ticket discounts, affect baggage demand; and how to optimize the relevant service resources of airports based on the research results of baggage flow, including by scientifically allocating check-ins, security, and human resources for different airlines and routes, can be critical directions for future research.
4. An ANN should be one of the best baggage flow prediction methods. An ANN can approximate any complex nonlinear function, which makes it more advantageous than other flow prediction methods in terms of its nonlinear mapping ability and generalization performance. At the same time, the application of the combined model based on an ANN overcomes the shortcomings of the single algorithm, forms a complementary advantage, and has huge development potential. Therefore, researchers believe that a combined model based on a neural network can solve complex time series problems such as airport baggage flow prediction.
5. An ANN has a wide range of applications. In the future, the development of the ANN prediction model may move forward in the following directions: by further improving the generalization ability of the ANN prediction model; by studying the standard algorithm for the optimal number of network layers and neural nodes to establish a combined forecasting model which is more suitable for the actual demand; and by designing a light and efficient neural network structure.  
The authors confirm the contributions to the paper are as follows: study conception and design: [PERSON] and [PERSON]; data collection and analysis: [PERSON] and J.Z.; draft manuscript preparation: [PERSON], J.F. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Author [PERSON] is employed by Chengdu Shuangliu International Airport Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.