## 2 The Current Research Status on Airport Baggage Demand
Meanwhile, [PERSON] found a correlation and time lag between passenger checked baggage demand and planned departure passenger flow in different day periods. At the same time, the short-term baggage demand model was established by the seasonal autoregressive integrated moving average (SARIMA) and gradient boosting decision tree. However, this study did not consider the impact of essential indicators such as holidays, working days, and winter and summer holidays on the baggage flow, nor did it consider the impact factors such as self-service baggage check-in. The lack of feature vector diversity led to the incomplete mapping relationship of the prediction model. [PERSON] proposed establishing prediction mechanisms for passenger check-in baggage demand to provide data support for fully tapping the passenger aircraft belly cabin carrying capacity [12]. This study used k-nearest neighbor (KNN) and density-based spatial clustering of applications with noise to interpolate the missing part of actual data. At the same time, particle swarm optimization (PSO) was used to find the optimal parameters to establish a baggage demand prediction model based on support vector machine regression (SVR). The results showed that the demand for checked baggage had the characteristics of daily similarity. On the one hand, the sample size needed to be larger. Two months of data from the self-service baggage check-in system and the departure area of the terminal were not considered. On the other hand, there needed to be more diversity in the selection of baggage-related feature vectors, which might not entirely reflect the scientific prediction of baggage demand. [PERSON] used the check-in baggage data and exploratory data and used the SVR algorithm to establish a baggage flow prediction model [13]. The comparison and verification of KNN and random forest algorithms concluded that the prediction result of SVR was the best among the three models. In summary, few studies have focused on the demand for checked baggage for airport departure passengers in previous research. Although some studies have suggested that the prediction results of airport departure passenger flow could be directly converted into the prediction results of baggage demand, relevant research was still needed to give a scientific quantitative conversion formula. On the one hand, the influencing factors and changing rules of baggage demand had their characteristics, as shown in Figure 1. Figure 1 compares the departure passenger flow and baggage flow of the Chengdu Shuangliu International Airport (the annual passenger throughput exceeds 30 million) in different months of 2018 (data prior to the COVID-19 pandemic outbreak). Three peaks in passenger flow appear in March, August, and October. By comparison, there are only two main peaks in baggage flow in February and October. Although the increase in passenger flow would increase the amount of baggage, it was not a simple linear relationship. Simply predicting baggage flow based on the prediction results of passenger flow would not only lead to significant errors in the conversion process but also ignore the characteristics of baggage demand. On the other hand, relevant research did not consider the combined effect of various influencing factors on baggage flow. At the same time, it established mathematical models by using simple mathematical statistics and hypothesis testing, resulting in insufficient sample diversity and poor prediction accuracy, which could not be put into production. Therefore, it is necessary to accurately and comprehensively grasp the airport departure baggage demand and deeply analyze the change in law and mechanism of baggage demand, which has important theoretical and practical significance in the current era of rapid development of the civil aviation industry. For example, accurately understanding baggage flow demand enables airport management to rationally allocate resources for check-in, security checks, and baggage handling systems.