# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 4 Evaluation Results and Discussion
### The Evaluation Results
#### 4.1.2 Causal Models  
The accuracy of the causal models is mainly influenced by the choices of variables. The relationship between the variables and APT should be first studied. Many studies tried to solve the relationship. [PERSON] et al. (2018) analyzed the relationship between APT and indicators of socio-economic development in each province of China. The result showed that the APT was strongly positively correlated with the GDP, urbanization rate and population density and the APT per unit GDP was weakly correlated with the GDP, urbanization rate and population density. In this paper, the relationship is comprehensively researched. GDP, GDP of primary industry, GDP of secondary industry, GDP of tertiary industry, resident population, year-end population, urbanization rate, imports and exports, disposable income, total retail sales of consumer goods, tourist visits and tourism income are selected as the variables. The correlation analysis is conducted on the APT of the airport and the indicators of the province where the airport is located. Usually, when the correlation coefficient between two variables is larger than 0.8, the two variables are believed to be  
Figure 4: The four airports which are unavailable predicted by the time series models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Criterion 1**} & \multicolumn{2}{c}{**Criterion 2**} & \multicolumn{2}{c}{**Criterion 3**} & \multicolumn{2}{c}{**Criterion 4**} \\ \cline{2-9}  & \multicolumn{1}{c}{**Number of Good**} & \multicolumn{1}{c}{**Percentage**} & \multicolumn{1}{c}{**Number of Good**} & \multicolumn{1}{c}{**Number of Good**} & \multicolumn{1}{c}{**Percentage**} & \multicolumn{1}{c}{**Number of Good**} & \multicolumn{1}{c}{**Number of Good**} & \multicolumn{1}{c}{**Percentage**} \\ \hline Summation of & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ all levels & 145 & 71\% & 149 & 73\% & 151 & 74\% & 150 & 74\% \\ \hline APT less than & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\
2 million & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ APT between & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\
2 million and & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\
10 million & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ APT more than & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\
10 million & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The applicability of time series models on different levels of airports. highly correlated. The correlation coefficients between APT and indicator are shown in Table 4. It is shown that the APT of an airport is highly correlated to the GDP of the city where the airport is located. In addition, the higher the magnitude of airport passenger throughput, the better the correlation between passenger throughput and macro indicators.