## 5 Conclusions and Discussion
### Discussion  
The results of this study indicate several important insights into pedestrian avoidance behavior on school roads, which have implications for traffic safety and management. 1. Model Performance Implications  
The superior performance of the GA-BP model can be attributed to the genetic algorithm's ability to optimize the weights and thresholds of the BP neural network, enabling faster convergence and higher accuracy. Despite its traditional success in many applications, the BP model struggled with complex pedestrian behavior prediction, highlighting the importance of incorporating optimization techniques like genetic algorithms. XGBoost, although effective in more straightforward tasks, demonstrated less flexibility in handling the intricate dynamics of pedestrian interactions with traffic. This suggests that for future research and real-world applications, combining neural networks with optimization techniques, such as GA-BP, may provide more reliable predictions in dynamic environments like school roads. 2. The Role of Traffic Volume and Speed  
The dominant role of traffic volume and vehicle speed in influencing pedestrian avoidance behavior is evident in this study. Pedestrians are highly sensitive to traffic density; higher traffic volumes result in more cautious behavior. Similarly, the speed of vehicles, especially in the same direction, influences pedestrians' timing and decision-making process. This observation is crucial for traffic planning and road safety measures, especially during peak hours when pedestrian-vehicle interaction is critical. 3. Vehicle Type and Pedestrian Avoidance  
Features related to vehicle types, such as whether the vehicle is a car or an electric bicycle, play a significant role in how pedestrians perceive risk and adjust their behavior. This study shows that pedestrians are more likely to take risks when encountering smaller vehicles like electric bicycles due to their perceived lower danger. This finding suggests that traffic regulations and safety measures should account for the different types of vehicles, as their interaction with pedestrians differs significantly. Furthermore, ensuring that vehicles, including electric bicycles, adhere to safe speed limits is crucial for reducing pedestrian risk. 4. Implications for Pedestrian Safety on School Roads  
The findings from this study have important implications for pedestrian safety, particularly in school zones. Measures to control traffic volume, such as restricting vehicle access during peak hours or implementing speed limits, could significantly reduce pedestrian risk. Additionally, the type of vehicles in school zones should be monitored and regulated, with specific measures to ensure that electric bicycles and other small vehicles do not pose an increased risk to pedestrians. Future policies should prioritize improving pedestrian infrastructure and traffic management during school peak hours to enhance safety for school children. 5. Future Research Directions  
This study highlights the need for further investigation into the behavioral dynamics of pedestrians in school zones. Future research could explore integrating real-time traffic data and environmental factors, such as weather conditions and pedestrian crowding, to improve model accuracy. Further analysis of less influential features (e.g., SM, OM) could provide deeper insights into pedestrian behavior and lead to more robust safety strategies. In conclusion, this study provides valuable insights into the factors influencing pedestrian avoidance behavior on school roads and demonstrates the potential of GA-BP models for accurate prediction. By addressing the identified key features, traffic management strategies can be developed to enhance pedestrian safety, especially in high-risk environments such as school zones. **Author Contributions:** Writing--original draft, G.L. ; Writing--review & editing, M.L. ; Funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.