## 5 Results and Discussion
### Cluster Analysis
#### 5.1.2 Latent Profile Descriptive Associations  
We performed multinomial regression analysis using IBM SPSS Statistics 26 to identify factors for driver classification. The following independent variables were tested in the MNL model: gender, age, monthly income, the highest level of education, driving experience, annual driving mileage, and CAV experience. Table 8 presents the multinomial regression results and uses the \"neutral\" cluster as the reference group. A detailed discussion about the coefficients of the attributes follows. As depicted in Table 8, the gender, age, highest level of education and annual driving mileage parameters did not achieve statistical significance, suggesting that no difference in the distribution of the above-described socio-demographic characteristics was found within the clusters. In other words, the above-mentioned factors did not result in the heterogeneity of psychological characteristics when facing the CAVs. Regarding the effects of monthly income, the results indicated that compared to the respondents with a relatively high income (over 30,000 RMB per month), the respondents with a low income (less than RMB 5000 per month) and middle income (RMB 5000-10,000 per month) were more likely to be negative towards CAVs (cluster 1). Regarding the effects of the driving experience, compared with the respondents with 1-5 years of driving experience, those who had driven for over 10 years were significantly more likely to be negative towards CAVs (cluster 1). As revealed by the above result, drivers who had been driving in the conventional traffic for a long time would face more difficulty adapting to a novel traffic system mixed with CAVs and HDVs. Regarding the effects of the CAV experience, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & & \multicolumn{3}{c}{**Cluster 1**} & \multicolumn{3}{c}{**Cluster 3**} \\ \cline{3-8}  & & **OR** & **CI (95\%)** & **Sig.** & **OR** & **CI (95\%)** & **Sig.** \\ \hline \multirow{4}{*}{Gender} & Female & 0.556 & 1.744 & \multirow{4}{*}{0.341} & \multirow{4}{*}{0.248} & \multirow{4}{*}{1.282} & \multirow{4}{*}{0.341} \\  & Male 1  & 0  & & & 0  & \\  & 18–23 & 18.267 & 85,796,668.249 & 0.997 & 0.616 & 1.852 & 0.449 \\  & 24–30 & 18.161 & 77,150,478.695 & 0.997 & 0.637 & 1.890 & 0.383 \\  & 31–40 & 16.750 & 18,815,132.623 & 0.997 & 1.056 & 2.875 & 0.133 \\  & 41–50 & 17.429 & 37,080,784.391 & 0.997 & 0.275 & 1.317 & 0.707 \\  & 51 or older 1  & 0  & 0  & & & 0  & \\  & Less than v5000 & 15.117 & 3,675,768.719 & 0.000 & 0.225 & 1.252 & 0.856 \\  & v5000 to v9999 & 16.443 & 13,837,355.877 & 0.000 & 0.545 & 1.724 & 0.658 \\  & V10,000–v29,999 & 16.319 & 12,229,100.069 & - & 0.707 & 2.028 & 0.565 \\  & V30,000 or more 1  & 0  & 0  & 0  & & & 0  & & & & \\  & High School Graduate & 0.003 & 1.003 & 0.998 & 1.059 & 2.885 & 0.089 \\  & or GED & 0.727 & 2.070 & 0.498 & 0.287 & 1.332 & 0.539 \\  & College & 0.315 & 1.370 & 0.670 & 0.117 & 1.125 & 0.706 \\  & Masters or Doctorate 1  & 0  & 0  & & & 0  & \\  & Less than one year & \(-20.400\) & \(1.382\times 10^{-9}\) & 0.993 & 0.072 & 1.075 & 0.917 \\ Driving experience & 1–5 years & \(-2.669\) & 0.069 & 0.033 & 0.404 & 1.498 & 0.464 \\  & 6–10 years & \(-1.545\) & 0.213 & 0.158 & 0.763 & 2.144 & 0.145 \\  & \textgreater{}10 years 1  & 0  & 0  & 0  & & & 0  & & & & \\  & Have driver license but & 1.810 & 6.113 & 0.102 & \(-0.044\) & 0.956 & 0.935 \\  & never driving & & & & & & \\ Annual driving & Less than 10,000 km & \(-0.205\) & 0.815 & 0.841 & \(-0.553\) & 0.575 & 0.245 \\ mileage & 10,000–30,000 km & \(-0.662\) & 0.516 & 0.530 & \(-0.058\) & 0.944 & 0.894 \\  & 30,001–50,000 km & 0.505 & 1.657 & 0.642 & \(-0.110\) & 0.895 & 0.840 \\  & \textgreater{}0,000 km 1  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  & 0  \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Multinomial logistic regression—the effects of socio-demographic characteristics.