## 5 Modelling of Lane-Changing Behaviours (LCB)
### Development of LCB Model
In these scenarios, the lighting effects proposed in the model appear to encourage drivers to change lanes in dangerous sections to avoid accidents. Additionally, driver experience positively impacts safe LCB in these scenarios, where lighting guides drivers through hazardous tunnel sections. The statistically significant parameters in these scenarios indicate that drivers tend to perform safer lane-changings by adhering to signs and markings in dangerous road sections. In particular, driving skills and experience play a critical role in helping drivers follow the guidance provided by traditional tunnel lighting and wall lighting, ensuring safer LCB. As shown in Table 10, the predicted probabilities of LCB for females are higher than those for males in all three scenarios, indicating that female drivers have a greater tendency to change lanes (\(p>0.10\)). The results also showed that prior experience driving through the Zigana Tunnel, which was simulated in the experiment, had no statistically significant effect on LCB across the scenarios. This finding was consistent for all scenarios. Additionally, when examining simulator experience, one of the categorical variables, it was found that previous use of a vehicle simulator was a significant factor influencing LCB in hazardous tunnel situations. When considering driving ability, another categorical variable, it was observed that drivers with lower ability were more likely to change lanes. This may be due to the fact that drivers who perceive themselves as having lower ability tend to be more cautious in tunnels and promptly change lanes when necessary, opting for the safest lane. Furthermore, analysis of driver characteristics, one of the key parameters, revealed that drivers with an aggressive profile are more likely to change lanes. This is consistent with the behaviour of aggressive drivers, who tend to frequently switch lanes in an effort to drive faster [9; 50]. When Table 10 is examined in detail, it becomes clear that there is little difference between the predicted and observed results, indicating that the proposed model is reliable and can be effectively used. Figure 10 shows the predicted probability values for LCB based on continuous variables across the three scenarios. Figure 10 presents the predicted probability values for vehicle speeds in each scenario. It can be seen that in S-1 and S-2, the probability of lane-changing increases as vehicle speed increases. However, in S-3, the probability of lane-changing decreases with increasing speed. This result aligns with the fact that in S-3, drivers tend to choose the correct lane (Lane 2) early on and remain in that lane. When analysing the probability of lane-changing according to the drivers' age, it can be concluded that the probability increases with age in S-2 and S-3, while in S-1, age does not have a significant effect. In other words, the lane-changing probability in S-1 appears to be independent of age. According to Figure 10c, driver experience has an inverse effect on LCB across all three scenarios, with the probability of lane-changing decreasing as experience increases. This may be explained by the fact that more experienced drivers are better  
at selecting the appropriate lane at the right time in hazardous road situations, leading to fewer lane-changing overall. Numerous studies have shown that experienced drivers exhibit better lane selection and LCB compared to less experienced drivers in various traffic scenarios [4; 50; 51; 52].