## 2 The Current Research Status on Airport Baggage Demand  
Existing research on air transportation flow mainly focuses on flight flow, passenger flow, and cargo flow, and there are few studies on baggage flow. In the study of the intelligent system of terminal baggage safety inspection, [PERSON] mentioned that the baggage of departing passengers should be quantified to facilitate classification research [2]. Meanwhile, the quantity, quality, and size of the baggage were quantified through software development, and the baggage location was calibrated for convenient and quick extraction. However, this study focused on software development and should have discussed the specific indicators of baggage in depth. [PERSON] et al. established a model to evaluate the time behavior characteristics of passenger flow and baggage flow in terminal buildings [3]. Meanwhile, they proposed reducing passenger checked baggage queuing time by controlling the number of check-in counters. [PERSON] et al. believed that baggage flow is strongly related to airport passenger flow and presents a certain proportion [4]. They believed the baggage demand prediction method could adopt the forecasting model of passenger flow. Then, a scientific forecasting model of passenger baggage was obtained through the test and simulation verification of actual data. When [PERSON] et al. studied the control scheme of the baggage handling system, they briefly introduced the passenger and baggage requirements in the precise time optimization method of the baggage coding trolley [5]. [PERSON] studied the demand characteristics of checked baggage and its influencing factors based on flights through the artificial neural network (ANN) method [6]. Therefore, it was found that the baggage flow was significantly related to the passenger flow, and the baggage demand was random and periodic. However, the research mainly obtained sample data using a sampling and questionnaire survey, and the granularity was large. [PERSON] et al. analyzed the baggage flow of an international airport in May 2012 using two prediction models, the back-propagation neural network (BPNN) and multiple regression [7]. Through cross-comparison of three flight data (all flight data, single flight data, and flight data of the same destination), it was found that the flight data of the same destination can more accurately predict the baggage flow. However, this study only made a simple prediction of the outbound checked baggage flow at the airport. Because of its data limitations, the stability of the prediction model could not be guaranteed, including influencing factors and sample sets. [PERSON] et al. analyzed the security inspection performance with the baggage security inspector as the research object [8]. They found that the shift length of the security inspector was directly proportional to the baggage flow and inversely proportional to the security inspection performance. [PERSON] et al. established a baggage weight distribution model and a time distribution model based on the number of baggage by mathematical statistics [9]. His research showed that the time required for baggage check-in obeyed the Burr distribution, and the number of checked baggage was positively correlated with the choice rate of the check-in counter. [PERSON] et al. conducted a field survey on the passenger flow characteristics of the departure and arrival terminals at a hub airport in China [10]. They evaluated the overall passenger flow of the terminal through the probability distribution of passenger flow intensity and residence time. Meanwhile, it was mentioned that the check-in time of baggage accounts for about one-third of the total time of departure passengers, and the passenger flow and baggage volume in the arrival area were closely related to the size of the aircraft model. [PERSON] analyzed the time distribution characteristics of the baggage demand of departing passengers through sample data such as manual check-in passenger flow, flight volume, and flight schedule [11].