## 3 Data Source  
### Data Collection  
This study collected highway passenger volume data from 1990 to 2022 in cities across the country from the China Statistical Yearbook and obtained a series of factor data that may affect passenger volume, including urban population, GDP, private car ownership, air passenger volume, and railway passenger volume. Considering the possible complex correlation between the influencing factors of highway passenger volume, it is necessary to select the appropriate influencing factors to facilitate data processing. The principles of data collection in this paper are as follows: (1) The collected influencing factors are all related to the highway passenger volume and have a significant impact. (2) Comparable and quantifiable influencing factors are collected. (3) It is required to easily collect the selected influencing factors from the China Statistical Yearbook. Most importantly, some non-quantifiable factors can be compared by converting them into quantifiable factors. In addition, subjective judgment and personal preference are non-quantifiable factors, so errors cannot be avoided. Therefore, this paper only selects quantifiable influencing factors to study the prediction of highway passenger traffic volume. To improve the performance of the prediction model, this study performed feature selection, that is, selecting the most relevant features from all possible influencing factors, and collecting data as shown in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Literature Serial Number** & **Research Object** & **Research Conclusions** \\ \hline \multirow{6}{*}{Multilayer Perceptron Neural Network} & 9 & Precipitable water vapor & The MLP model has good accuracy and adaptability in southern China. \\  & 10 & Medical record connotation quality & The multilayer perceptron neural network \\  & 10 & Medical record connotation quality prediction accuracy is high. \\  & & The weighted average temperature in & The accuracy and stability of the model in \\  & 11 & Southwest China & Southwest China are better than those of Bevis \\  & 12 & Personal credit & The classification result of multilayer perceptron is \\  & 13 & Fast classification method of & better than that of discriminant analysis. \\  & 13 & multi-spectral images & The integrated classifier is better. \\ \hline \multirow{6}{*}{BP Neural Network} & 5 & Highway passenger volume & The softplus double hidden layer neural network \\  & 6 & Highway passenger volume of Gansu Province & The prediction results are better than the multiple \\  & 7 & Highway passenger volume and & The feasibility of neural network prediction \\  & 7 & freight volume & is high. \\  & 8 & Highway passenger volume & The minimum relative error is 1.1\%, and the \\  & 16 & Highway passenger volume & Average relative error is 2.78\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The research results of some of the literature. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**GDP**} & \multirow{2}{*}{**Population**} & **Private Car** & **Railway Passenger** & **Air Passenger** & **Highway** \\  & & & **Ownership** & **Volume** & **Volume** & **Passenger Traffic** \\ \hline
1990 & 18,872.9 & 114,333 & 81.62 & 95,712 & 1660 & 648,085 \\
1991 & 22,005.6 & 115,823 & 96.04 & 95,080 & 2178 & 682,681 \\
1992 & 27,194.5 & 117,171 & 118.2 & 99,693 & 2886 & 731,774 \\
1993 & 35,673.2 & 118,517 & 155.77 & 105,458 & 3383 & 860,719 \\
1990 Since the data in the statistical yearbook is aggregated from the entire district or city, the administrative region is used as the basic unit of data collection. The results showed that 3444 samples were collected from 280 administrative regions, including the 5 influencing factors and highway passenger volume. The record date is from 1990 to 2022, covering 33 years.