# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 5 Conclusions  
Time series models, causal models, artificial intelligent models, market share methods and analogy-based methods are all commonly used methods for APT prediction. In the paper, a hybrid method of the aforementioned methods is developed and investigated. By conducting the evaluation of five kinds of models to predict the APT of two hundred three airports in China, it is found that for most prediction scenes, the models are applicative to the short-term prediction of APT and the accuracy does not improve with the complexities of the models. Overall, 88% of the studied airports can be effectively predicted by using the evaluated prediction methods, and the MAPE is mostly within 10%. In addition, the constructed evaluation method can effectively predict the airport passenger throughput of more than 10 million. The higher the airport passenger throughput level is, the more effective the segmentation prediction methods can be. The performance of the mentioned models in this paper is bad when there are insufficient historical data for modelling, or the APT of the airports changes abruptly owing to expansion, relocation or other kinds of external forces like earthquakes. When the airport enters the stable development period, the number of available prediction models in the hybrid method is significantly increased. The results show that there is no relationship between the prediction accuracy and the complexity of prediction models. Among the five used models, the time series model, causal models and market share method usually have higher applicability than the other two models. The findings provide potential support for the selection of prediction models for the APT prediction. The prediction and evaluation methods in this paper are usually applicable to airports with high APT, stable development periods and sufficient historical \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**0-5\%**} & \multicolumn{2}{c}{**5-10\%**} & \multicolumn{2}{c}{**10-15\%**} & \multicolumn{2}{c}{**15-20\%**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Number of Good Models**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{2}{c}{**Number of Good Models**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{2}{c}{**Number of Good Models**} & \multicolumn{2}{c}{**Percentage**} \\ \hline  & Summation & & & & & & & & \\  & of all levels & & & & & & & & \\  & APT less & & & & & & & & \\  & than & & & & & & & & \\  & 2 million & & & & & & & & \\ Market & APT & & & & & & & & \\ share & between & & & & & & & & \\ methods & 2 million & 27 & 79\% & 2 & 6\% & 1 & 3\% & 0 & 0\% \\  & and & & & & & & & & \\  & 10 million & & & & & & & & \\  & APT more & & & & & & & & \\  & than & & 65 & 50\% & 14 & 11\% & 4 & 3\% & 1 & 1\% \\  & 10 million & & & & & & & & \\ \hline  & Summation & & & & & & & & & \\  & of all levels & & & & & & & & \\  & APT less & & & & & & & & \\  & than & 14 & 36\% & 10 & 26\% & 7 & 18\% & 3 & 8\% \\  & 2 million & & & & & & & & \\  & APT & & & & & & & & \\  & between & 2 million & 2 & 6\% & 2 & 6\% & 6 & 18\% & 7 & 21\% \\  & and & & & & & & & & & \\  & APT more & & & & & & & & \\  & than & 8 & 6\% & 10 & 8\% & 8 & 6\% & 11 & 8\% \\  & 10 million & & & & & & & & \\ \hline  & Summation & & & & & & & & \\  & of all levels & & & & & & & & \\  & APT less & & & & & & & & \\  & than & 4 & 10\% & 1 & 3\% & 1 & 3\% & 1 & 3\% \\ Artificial & APT & & & & & & & & \\  & between & & & & & & & & \\ models & 2 million & & & & & & & & \\  & and & & & & & & & & \\  & 10 million & & & & & & & & \\  & APT more & & & & & & & & \\  & than & & & & & & & & \\  & 10 million & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 19: _Cont._data. Further studies should be made to build prediction and evaluation methods when the external environment is unstable under the influence of COVID-19 and other external forces such as earthquakes or without sufficient historical data. Conceptualization, B.C. and J.W. ; methodology, B.C. ; software, B.C. ; validation, B.C., J.W. and X.Z. ; formal analysis, X.Z.