## 3 Research Methodology
### Research Method  
Compared with the traditional linear regression model, the Logit model is more suitable for studying problems where the dependent variable is a binary variable. Therefore, the Logit model is well-suited for analyzing issues with binary outcomes: 1 (using public transportation) and 0 (driving all the way). However, it cannot accurately explain the influence of drivers' perception, attitude, and other psychological variables on travel behavior [37]. By comparing the existing literature, it is predicted that including psychological factors can more effectively explain individual travel patterns compared to when psychological factors are absent [38; 39]. SEM is an important method for multivariate analysis. It can quantify the psychological underlying factors of drivers in river-valley-type cities. Its advantage lies in its ability to estimate the relationship between multiple variables simultaneously and test the relationship between multiple variables in a single model [40]. However, if only the influence of a single SEM is analyzed, and the relationship between the influencing factors of multiple aspects considered in this paper is analyzed simultaneously, the model will become more complex. The structural relationship between each factor becomes difficult to clarify, and the calculations become too extensive. Additionally, the relationship between different factors may be overlooked, leading to redundancy or duplication in factor construction [41]. Therefore, in this paper, based on the Logit model, SEM is introduced to describe the impact of potential psychological perception variables on motorists' P&R decision-making. Four latent variables, namely, security perception (SEC), convenience perception (CON), comfort perception (COM), and accessibility perception (ACC), are utilized to elucidate the terrain spatial perception of motorists in river-valley-type cities using P&R. The Logit model examined the effects of the psychological attributes mentioned above, as well as the motorists' individual socio-economic attributes, objective built-environment attributes, and travel characteristics on P&R use behavior. It also analyzed the moderating effects of terrain spatial perception on other factors influencing P&R use behavior (refer to Figure 2). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Variable Symbol** & & **Question Description of the Corresponding Observed Variables** \\ \hline \multirow{4}{*}{Security perception} & SEC1 & The P\&R parking lot has complete surveillance lighting facilities. \\  & SEC2 & An underpass between parking and pick-up points makes P\&R safer. \\  & SEC3 & Using P\&R, which has less risk of being early or late, provides more \\  & & psychological security. \\ \hline \multirow{4}{*}{Convenience perception} & CON1 & It is less affected by the blockage at the narrow junction of urban groups when using P\&R. \\  & CON2 & It is less affected by rivers in river–valley-type cities when using P\&R. \\  & CON3 & It is easier to find a parking space when using P\&R. \\  & CON4 & Other services are provided near the P\&R parking lot. \\ \hline \multirow{4}{*}{Comfort perception} & COM1 & The stability of using P\&R is better. \\  & COM2 & Using P\&R is not affected by peak hour congestion and does not require a long ride time. \\  & COM3 & There is no driving pressure caused by complex road conditions when using P\&R. \\ \hline \multirow{4}{*}{Accessibility perception} & ACC1 & Using P\&R travel between urban groups can enable users to reach the destination faster. \\  & ACC2 & The traffic around P\&R parking lots is good. \\ \cline{1-1}  & ACC3 & It is less affected by the one-way street and the broken road when using P\&R. \\ \cline{1-1}  & ACC4 & It is less affected by all aspects of the spatial terrain than driving all the way. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of observed variable. SEM mainly consists of two parts: the measurement model and the structural model.