# Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption
## 4 Results
### EVTR Travel Choice Prediction Using Two Mixed Logit Models
#### 4.2.2 EVTR Travel Selection Model with Latent Variables  
The selection model with latent variables is constructed using the mixed logit model, which takes the social population attributes, travel characteristics, travel environment, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **B** & **S.E.** & **Sig.** & **Exp (B)** \\ \hline Constant & \(-\)1.600 & 1.052 & 0.013 & 0.202 \\ \(X_{gen}\) & 0.052 & 0.180 & 0.038 & 1.053 \\ \(X_{age}\) & \(-\)0.019 & 0.087 & 0.043 & 0.981 \\ \(X_{inc}\) & 0.029 & 0.100 & 0.027 & 1.029 \\ \(X_{chch}\) & 0.081 & 0.110 & 0.046 & 1.084 \\ \(X_{chi}\) & 0.154 & 0.199 & 0.064 & 1.166 \\ \(X_{car}\) & \(-\)0.486 & 0.186 & 0.030 & 0.615 \\ \(X_{sta}\) & \(-\)0.121 & 0.103 & 0.023 & 0.886 \\ \(X_{uch}\) & 0.012 & 0.081 & 0.028 & 1.012 \\ \(X_{lno}\) & 0.314 & 0.084 & 0.018 & 1.369 \\ \(X_{lM1}\) & 0.090 & 0.110 & 0.041 & 1.094 \\ \(X_{lM2}\) & 0.075 & 0.110 & 0.044 & 1.078 \\ \(X_{lM3}\) & 0.169 & 0.109 & 0.012 & 1.184 \\ \(X_{lM4}\) & 0.082 & 0.111 & 0.046 & 1.085 \\ \(X_{lM5}\) & 0.152 & 0.140 & 0.017 & 1.164 \\ n & & & 619 & \\ Log likelihood & & & 742.995 & \\ McFadden’s R2 & & & 0.273 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Parameter fitting results of binary logit model without attitudinal variables. and attitude latent variable as independent variables, and whether travelers choose EVTR travel as an independent variable. The SPSS is used to calibrate the model. After multiple modeling and screening to remove the independent variables that have no significant influence on the dependent variable, the parameter estimation and test results of the ordered logical model are finally established in Table 8. The number of samples was 619. The McFadden R2 = 0.341 > 0.2 [43], which shows that the precision of the mixed logit model is acceptable. For the observed variables, age, gender, education level, individual income, marriage, and child have no significant impact on EVTR adoption. Further, the automobile available, EVTR station density, fleet size, and familiarity significantly impact EVTR adoption. These findings suggest that travel characteristics influence travelers' adoption of EVTRs more than other socioeconomic attributes. From the calibration results of model parameters containing latent variables, it can be found that not all latent variables have significant effects. Perceived comfort (0.387), perceived efficiency (0.211), subjective evaluation (0.165), use preference (0.174), and use willingness (0.023) have significant positive impacts on EVTR use demand. Station inconvenience (\(-\)0.005), station inconvenience (\(-\)0.121), use inconvenience (\(-\)0.277), and personal inconvenience (\(-\)0.263) have significant negative impacts. Social pressure (\(\text{Sig. }=0.086>0.5\)) has no significant impact. Therefore, in addition to improving the coverage of rental stations and fleet size, increasing the publicity and promotion of EVTRs, enhancing the perception level of travelers on the perceived usefulness and availability of EVTRs, and reducing the perception level of travelers on travel inconvenience are essential contents to promote the development of shared car demand. Lastly, we compare the results of the respondents' choice obtained from the survey data and the logit model fitting and prediction. The goodness of fit of the binary logit model without latent attitude variables and mixed logit model with attitude latent variables is