## 6 Results and Discussions  
A total of 24 variables are considered. All dummy variables are transformed to binary indicator variables, and numbers are appended to the variable names to distinguish the indicator variables (e.g., N\({}_{\rm LLC}\)1, N\({}_{\rm RLC}\)1). Prior to modeling, the correlations between the variables are analyzed, and those with high correlations are removed. In addition, Z-score is used to standardize continuous data in order to eliminate the effects of dimension and make them comparable with other variables. The results of the best-fitted random parameters ordered logit (RPOL) models and RPOL models with heterogeneity in means (RPOLMH) are shown in Tables 6-10, as the variance heterogeneity is not statistically significant. These results consist of the estimation of parameter, t-statistics, and goodness-of-fit statistics (simulated log-likelihood at convergence, restricted log-likelihood, McFadden's pseudo R\({}^{2}\), and AIC). On the basis of the RPOLMH models, likelihood ratio tests show that the null hypothesis of the performance of four models (Tables 7-10) are the same with that of the combined model (Table 6) which is rejected with over 99.9% confidence, indicating the separation of different vehicle-following patterns is reasonable. \begin{table}
\begin{tabular}{l l l l} \hline \(\mathbf{y_{1}}\) & \(\mathbf{y_{2}}\) & & & \\  & **D\({}_{1}\) (0–120 m)** & **D\({}_{2}\) (120–240 m)** & **D\({}_{3}\) (240–360 m)** \\ \hline D\({}_{1}\) (0–120 m) & – & 36.378 (14) & 30.849 (15) \\  & & [99.90\%] & [99.08\%] \\ D\({}_{2}\) (120–240 m) & 30.608 (13) & – & 38.68 (15) \\  & [99.61\%] & & [99.93\%] \\ D\({}_{3}\) (240–360 m) & 37.190 (13) & 54.555 (14) & – \\  & [99.96\%] & [>99.99\%] & \\ \hline \end{tabular} Notes: the numbers in parentheses represent the degrees of freedom for the Likelihood Ratio Test.  
\end{table} 
Table 4: Likelihood ratio test results between different diverging areas (MTC-MTC model). \begin{table}
\begin{tabular}{l l l l} \hline \(\mathbf{y_{1}}\) & \(\mathbf{y_{2}}\) & & \\  & **D\({}_{1}\) (0–120 m)** & **D\({}_{2}\) (120–240 m)** & **D\({}_{3}\) (240–360 m)** \\ \hline D\({}_{1}\)(0–120 m) & – & 50.473 (13) & 102.1 (14) \\  & & [>99.99\%] & [>99.99\%] \\ D\({}_{2}\)(120–240 m) & 25.011 (11) & – & 40.862 (14) \\  & [99.09\%] & & [99.98\%] \\ D\({}_{3}\)(240–360 m) & 23.345 (11) & 38.282 (13) & – \\  & [98.42\%] & [99.97\%] & \\ \hline \end{tabular} Notes: the numbers in parentheses represent the degrees of freedom for the Likelihood Ratio Test.  
\end{table} 
Table 3: Likelihood ratio test results between different diverging areas (ETC-MTC model). \begin{table}
\begin{tabular}{l l l l} \hline \(\mathbf{y_{1}}\) & \(\mathbf{y_{2}}\) & & \\  & **D\({}_{1}\) (0–120 m)** & **D\({}_{2}\) (120–240 m)** & **D\({}_{3}\) (240–360 m)** \\ \hline D\({}_{1}\) (0–120 m) & – & 36.236 (13) & 41.594 (14) \\  & & [99.95\%] & [99.99\%] \\ D\({}_{2}\) (120–240 m) & 43.114 (12) & – & 41.666 (14) \\  & [>99.99\%] & & [99.99\%] \\ D\({}_{3}\) (240–360 m) & 64.023 (12) & 38.314 (13) & – \\  & [>99.99\%] & [99.97\%] & \\ \hline \end{tabular} Notes: the numbers in parentheses represent the degrees of freedom for the Likelihood Ratio Test.  
\end{table} 
Table 5: Likelihood ratio test results between different diverging areas (MTC-ETC model).