## 4 Case Study
### Data Processing  
A combination of quantitative and qualitative analyses was used here for data selection. Qualitative methods were used first. The time series data on sea-rail intermodal container throughput for Yingkou port in Liaoning were selected as the research object for feature selection, and the data were obtained from Liaoning Statistical Yearbook, National Bureau of Statistics, with the relevant statistical data provided by officials for the years from 2014 to 2024. Here, the corresponding evaluation indicators were selected based on four division criteria: the transportation capacity, fixed-asset investment and construction, the economic development level, and international trade and openness. The selection of the evaluation indicators for the four divisional criteria took into account all the public data and indicators for Yingkou City, Liaoning Province, and the whole country, and 33 evaluation indicators were obtained after the statistical organization. The evaluation indicators used under each division standard are shown in Table 2. \begin{table}
\begin{tabular}{c c} \hline
**Dividing Indicators** & **Setting of Evaluation Indicators** \\ \hline \hline \multirow{7}{*}{Transportation capacity} & Yingkou port container throughput. \\  & Yingkou port cargo throughput. \\  & Yingkou port container terminal throughput. \\  & Road freight traffic in Liaoning Province. \\  & Liaoning waterway cargo volume. \\  & Liaoning waterway cargo turnover. \\  & Liaoning highway cargo turnover. \\  & Liaoning port cargo throughput. \\  & Liaoning port container throughput. \\  & China coastal container throughput. \\  & China railway cargo delivery. \\  & China railway cargo turnover. \\  & China coastal cargo throughput. \\ \hline \multirow{7}{*}{Fixed-asset investment and construction} & Liaoning fixed-asset investment. \\  & Fixed-assets investment in road, water, and land transportation \\  & in Liaoning. \\ \cline{1-1}  & Fixed-assets investment in road, water, and land transportation \\ \cline{1-1}  & in Yingkou. \\ \cline{1-1}  & China highways and waterways fixed investment. \\ \cline{1-1}  & China railway fixed-asset investment. \\ \cline{1-1}  & China fixed-asset investment. \\ \hline \end{tabular}
\end{table} 
Table 2: Factors affecting container throughput of Yingkou port sea–rail combined transport. The raw data were analyzed and some missing data were found. For the missing data, multiple interpolation was used to interpolate the missing data values. The fixed-assets investment in road, water, and land transportation in Yingkou and Liaoning, which had more missing values, were eliminated. An outlier analysis was performed on the supplemented data; the outliers were excluded and we continued to use multiple interpolation to make up the difference until there were no outliers in the data. The Pearson and Spearman correlation coefficients between each factor were obtained through the collated data. Because of the simultaneous use of multiple eigenvalues to predict the sea-rail intermodal container throughput, only the correlation coefficient values of the various influencing factors on the sea-rail intermodal container throughput were screened out, and the correlation coefficients of the influencing factors on the sea-rail intermodal container throughput were obtained after their collation, as shown in Table 3, which can be seen as the degree of influence of the different factors on the sea-rail intermodal container throughput at Yingkou port.