# The Role of Infrastructural and Psychological Factors in Sustainable Transportation Mode Choices
## 3 Results
### Effects of Spatial Factors and Psychological Variables on Mobility Behavior  
Two ordinal logistic regression analyses were conducted to investigate the general use of transportation modalities: one analysis for General PT Use and another for General MIT Usage. In both ordinal logistic regressions, the predictor variables center accessibility, railway accessibility, adaptability, climate change perception, and car orientation were considered. The predictor variables were tested a priori to verify that there was no violation of the assumption of no multicollinearity. Correlation coefficients between the predictors did not exceed 0.80, which would have indicated strong correlations and suggested multicollinearity (Table 2). Results are presented in Table 3 for the dependent variable PT usage and in Table 4 for MIT usage. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & \(M\) & \(SD\) & **Range** \\ \hline Center Accessibility & 23.98 & 14.78 & 1.54–77.14 \\ Railway Accessibility & 30.48 & 21.64 & 4.00–106.92 \\ Adaptability & 3.22 & 0.64 & 1.10–5.00 \\ Climate Change Perception & 6.00 & 0.85 & 2.20–7.00 \\ Car Orientation & 2.95 & 0.91 & 1.00–5.00 \\ General PT Use & 4.30 & 2.14 & 1.00–7.00 \\ General MIT Use & 4.82 & 1.99 & 1.00–7.00 \\ \hline \hline \end{tabular} Note. Lower values reflect better center and railway accessibility; PT = Public Transportation; MIT = Motorized Individual Transport.  
\end{table} 
Table 1: Descriptive Statistics. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Variable** & \begin{tabular}{c} **Center** \\ **Accessibility** \\ \end{tabular} & \begin{tabular}{c} **Railway** \\ **Accessibility** \\ \end{tabular} & \multirow{2}{*}{**Adaptability**} & \begin{tabular}{c} **Climate** \\ **Change** \\ **Perception** \\ \end{tabular} & \begin{tabular}{c} **Car** \\ **Orientation** \\ \end{tabular} & \
\begin{tabular}{c} **General PT** \\ **Use** \\ \end{tabular} &
\begin{tabular}{c} **General** \\ **MIT Use** \\ \end{tabular} \\ \hline Center Accessibility & - & & & & & \\ Railway Accessibility & \(\rho\) = 0.120 & - & & & \\ Adaptability & \(\rho\) = 0.125 & \(\rho\) = 0.206 ** & - & & & \\ Climate Change & \(\rho\) = 0.025 & \(\rho\) = 0.145 * & \(\rho\) = 0.064 & - & - & \\ Perception & \(\rho\) = \(-\)0.008 & \(\rho\) = 0.114 & \(\rho\) = 0.035 & \(\rho\) = \(-\)0.162 * & - & \\ General PT Use & \(\rho\) = \(-\)0.107 & \(\rho\) = \(-\)0.169 * & \(\rho\) = 0.027 & \(\rho\) = 0.030 & \(\rho\) = \(-\)0.181 * & - \\ General MIT Use & \(\rho\) = 0.225 ** & \(\rho\) = 0.210 ** & \(\rho\) = \(-\)0.098 & \(\rho\) = 0.010 & \(\rho\) = 0.359 ** & \(\rho\) = \(-\)0.581 ** & - \\ \hline \hline \end{tabular} Note. \(N\) = 187, presented are values of Spearman’s Rho (\(\rho\)) and their significances (\(p\)). PT = Public Transportation; MIT = Motorized Individual Transportation. * \(p\) < 0.05, ** \(p\) < 0.01.  
\end{table} 
Table 2: Correlation Matrix for the variables of interest. When focusing on General PT Use (Table 3) as the dependent variable, the model fit was statistically significant, \(\chi^{2}\) = 17.36, \(p\) = 0.004. The Pseudo \(R\)-Square values (_Nagelk-erke_ = 0.09) suggest a weak relationship between the predictors and General PT Use [46]. Higher car orientation significantly reduces the odds of PT usage (\(\beta\) = \(-\)0.375, \(p\) = 0.011), while higher railway accessibility is associated with increased PT usage (\(\beta\) = \(-\)0.016, \(p\) = 0.012 due to reverse-coded variable). Center accessibility does not hold up as a predictor when applying bootstrapping; therefore, no significant relation can be reported. Also, the psychological variables CCP show no predictive value within our model. Concerning the ordinal logistic regression analysis using General MIT Usage as the dependent variable, the model fit was also significant, \(\chi^{2}\) = 57.43, \(p\) < 0.001, with a strong relationship between the predictors and the outcome, _Nagelkexe_ = 0.27 [46].