## 5 Modelling of Lane-Changing Behaviours (LCB)  
### Variables and Model Determination  
In the study, an effort was made to analyze the parameters influencing Lane-Changing Behaviours (LCB) of drivers in the \"Zigana Tunnel\" under three different scenarios. To achieve this, the following variables were considered: lane-changing behaviour (yes/no) as the dependent variable; speed (km/h), age (years), and driver experience (years) as continuous independent variables; and route familiarity (yes/no), driving simulator experience (yes/no), gender (female/male), driving skill (beginner/medium/good/very good), and driver profile (aggressive/medium/calm) as discrete independent variables, as outlined in Table 6.  
To determine the effective parameters, both logit and probit regression models were applied separately. These models incorporate a combination of quantitative and qualitative  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **LCB** & **Lane-Changing Behaviour (LCB) as Dependent Variable** \\ \hline \multicolumn{3}{l}{Continuous variables} \\ \(\bullet\) & Speed (km/h), & Vehicle speeds inside the tunnel \\ \(\bullet\) & Age (year), & Driver age (years) \\ \(\bullet\) & Driver experience (year) & Driving license duration (years) \\ \hline \multicolumn{3}{l}{Categorical variables} \\ \(\bullet\) & Route familiarity & (Takes the value of 1 if individual is yes, 0 otherwise), \\ \(\bullet\) & Gender & (Takes the value of 1 if individual is female, 0 otherwise), \\ \(\bullet\) & Driving simulator experience & (Takes the value of 1 if yes, 0 otherwise) \\ \(\bullet\) & Driving skill 2 & (Takes the value of 1 if medium, 0 otherwise) \\ \(\bullet\) & Driving skill 3 & (Takes the value of 1 if good, 0 otherwise) \\ \(\bullet\) & Driving skill 4 & (Takes the value of 1 if very good, 0 otherwise) \\ \(\bullet\) & Driver profile 2 & (Takes the value of 1 if medium, 0 otherwise) \\ \(\bullet\) & Driver profile 3 & (Takes the value of 1 if calm, 0 otherwise) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Dependent and independent variables of LCB model.  
Figure 8: Lane utilization behaviour of drivers on three scenarios and their sections.  
variables and are known as analysis of covariance (ANCOVA) models [9; 42; 43]. Least squares (Ls) means were used to determine the parameters of the ANCOVA model. The suggested ANCOVA model was predicted as given in Equation (3). Input variables are also shown in Figure 9.  
\[LCB=\lambda_{0}+\lambda_{1}\;\textit{driving}\;\textit{familiar}\;\textit{on examined}\;\textit{route}+\lambda_{2}\;\textit{gender}+\lambda_{3}\;\textit{driving}\;\textit{simulator}\;\textit{experience} \tag{3}\] \[+\lambda_{4}\;\textit{driving}\;\textit{skill}\;2+\lambda_{5}\; \textit{driving}\;\textit{skill}\;3+\lambda_{6}\;\textit{driving}\;\textit{skill}\;4+ \lambda_{7}\;\textit{driver}\;\textit{profile}\;2\] \[+\lambda_{8}\;\textit{driver}\;\textit{profile}\;3+\sigma_{1}\; \textit{speed}+\sigma_{2}\;\textit{age}+\sigma_{3}\;\textit{driver}\;\textit{ experience}+\pi\]  
In the model equation, \(\lambda_{0}\) shows a constant term; \(\lambda_{i}\), \(i\
eq 0\) shows the coefficients of categorical variables; \(\sigma_{j}\), \(j\) = 1,..., 4 presents the coefficients of the variables in the model; and, finally, \(\pi\) indicates a disturbance term. Descriptive statistics (means, standard deviations, minimum and maximum values) of numerical variables of the conducted analysis are given in Table 7.