## 3 Empirical Analysis  
### Flight Data  
Our research utilized data on all domestic flights in China for the year 2018, obtained from VariFlight, the leading aviation data service provider in China. Flights from Hong Kong, Macau, and Taiwan regions were excluded. These data included the most extensive information regarding the number of flights and airports in China during that year. The data consist of 11 valid attributes that are presented in Table 1. The redundancy of some redundant variables, which were not related to our research objectives, could not be used in our research and could potentially affect data processing speed. To address this issue, the researchers removed the delay information.  
During the process of collecting, storing, and retrieving data, missing values, outliers, or inconsistencies are often encountered. To ensure the accuracy and effectiveness of the data, it is necessary to analyze the missing values, outliers, and duplicates to determine the validity of the data. The authors dealt with the data that had undergone attribute processing. These data contained a total of 4,961,074 data entries, standing for the total number of flights in 2018. Of the 35,715 records, 0.07% had missing actual take-off and landing times. The purpose of processing the data was to create a temporal network. While constructing the network, this study had to consider the following constraints: the departure time of the flights is after 4:00 a.m. in the morning, and the landing time is before 4:00 a.m. the following day. Only data that met these conditions were defined as valid data.  
A considerable amount of data in our available data was either missing, invalid, or redundant. Therefore, it was vital to conduct data preconditioning before data mining to improve data quality and satisfy the application requirements. The primary and most fundamental method to deal with missing data is to eliminate the samples that contain missed data. The existing data were mostly affected by missing values in actual takeoff and landing times. Upon comparison with actual operational data, it became evident that the presence of missing values was often observed in conjunction with distinct situations, such as diversion or midair returns. As a result, the missing data points were deemed invalid.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable Type** & **Variable Name** \\ \hline \multirow{2}{*}{Time Information} & Departure time information: Scheduled departure time, Actual departure time \\ \cline{2-3}  & Arrival time information: Scheduled arrival time, Actual arrival time \\ \hline \multirow{2}{*}{Delay Information} & Departure delay in seconds \\ \hline \multirow{4}{*}{Airport Information} & Landing airport information: Airport IATA code \\ \cline{2-3}  & Take-off airport information: Airport IATA code \\ \cline{2-3}  & Airport location information: Airport Longitude, Airport Latitude, Province, City \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of Data.  
Apart from missing values, data validity needed to be assessed. Hence, to comply with the aforementioned constraints, the researchers had to remove data collected before 4:00 a.m. on 1 January 2018, because they are not classified as belonging to 2018. In addition, the data between 0:00 and 4:00 a.m. on 1 January 2019, were not available, so the researchers removed data between 4:00 a.m. on 30 December 2018 and 0:00 on 31 December 2018 to ensure data accuracy (Data from the year 2018 are inclusive of 1 January 2018, from 4:00 a.m. to 31 December 2018, until 4:00 a.m.). After the completion of the data preprocessing phase, a total of 4,915,350 valid data records were obtained. These records represent 99.08% of the initial dataset.