# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 1 Introduction  
An accurately predicting result of passenger throughput for an airport is one of the most important factors for its development decision on construction and expansion. A larger predicted result will inevitably lead to a waste of resources such as untimely land occupations, idle facility constructions and lower returns on investments. When the prediction result is too small, the crowded airport terminal may bring about poor travel experiences as well as higher security risks. Therefore, conducting accurate predictions will effectively avoid economic losses caused by inappropriate decision-making during airport development [1]. The task for more accurate predictions has been studied since early ages. Predictions can be conducted both qualitatively by using methods such as executive judgement, market research and Delphi techniques, and quantitatively by using methods including time series forecasting [2, 3] and causal models [4, 5, 6]. To promote the precision of prediction and the adaptivity for different types of airports, hybrid approaches integrating multiple models are proposed. [PERSON] et al. [7] proposed a hybrid method assembling variational mode decomposition, autoregressive moving average model and kernel extreme learning machine, which showed a better result than the single-model methods. [PERSON] et al. [8] proposed two hybrid approaches based on seasonal decomposition and the least squares support vector regression model for short-term forecasting of air passengers. Their empirical analysis showed that the proposed hybrid approaches were better than other time series models. Currently, artificial intelligence is performing stronger ability on different prediction scenes. Artificial intelligence methods including support vector regression (SVR), Monte Carlo simulation, decision-making tree and deep learning are widely used for airport passenger throughput (APT) prediction [9, 10]. [PERSON] [11] employed an integrated mixture of localexpert models to forecast air passengers at Sao Paulo International Airport. The model was validated using out-of-sample data, and the accuracy of the generated predictions proved to be satisfactory. [PERSON] et al. [12] proposed a grey seasonal least square support vector regression, and it was highly recommended for addressing issues with periodic and nonlinear features. [PERSON] et al. [13] combined a Markovian model with a grey model and they found that a fuzzy Markovian model showed better performance on the observations with trends and intercepts. Meanwhile, big data are providing possibilities for real-time and more precise APT prediction, especially for specific short-time prediction scenes. [PERSON] et al. [14] related the data from search engines to short-time passenger demands and proposed a novel decomposition ensemble model to discuss the role of Internet search data in air passenger demand forecasting. The decomposition ensemble model obtained more accurate and reliable prediction results than the benchmark models. [PERSON] et al. [15] proposed a method for separating the two forces of COVID-19 and evaluating the respective impact on demand, dividing passengers into different segments based on passenger characteristics, simulating different scenes, and predicting demand for each passenger segment in each scene. [PERSON] et al. [16] used a time series model to study the difference between the demand that was observed during the pandemic, and the demand that was forecast based on the pre-pandemic trend. All models suggest that demand would have increased further without COVID-19. Since the development of airport passenger throughput is related to many stochastic factors, it is difficult to observe the causal links between APT and potential factors. In addition, the accuracies of different prediction methods cannot be compared without a clear and definite prediction scene. Therefore, studies have been conducted to judge the performance of different prediction methods.