## 1 Introduction  
In today's increasingly congested traffic environment, pedestrian safety, particularly for children commuting to and from school, has become a critical concern. Ensuring their safety in high-traffic areas is a top priority. When navigating roads with heavy traffic, pedestrians, especially children, must make crucial decisions about whether to avoid oncoming vehicles. This decision-making process intuitively reacts to the surrounding environment and plays a key role in traffic safety. Therefore, an in-depth investigation into pedestrian avoidance behavior is essential for improving road safety and reducing traffic accidents.  
Numerous studies have examined the risks associated with children's school commutes and pedestrian safety. For example, [PERSON] (2022) [1] highlighted the importance of safe routes to school, emphasizing the cognitive benefits of livable streets for schoolchildren. [PERSON] et al. (2022) [2] explored safety issues in specific built environments, underscoring the role of infrastructure in pedestrian safety. Similarly, [PERSON] et al. (2020) [3] proposed an ergonomic approach to evaluating walkable urban environments, which is essential for improving pedestrian accessibility and safety.  
Moreover, researchers have explored various technological interventions to mitigate these risks. Intelligent transportation systems, bright traffic signals, and community-based interventions have been implemented to enhance pedestrian safety. Advanced methodologies, such as deep learning and machine learning [4], have also been applied to analyze and predict pedestrian behaviors. For instance, [PERSON] et al. (2016) [5] utilized Social LSTM (Long Short-Term Memory) to predict human trajectories in crowded spaces, which has implications for improving children's traffic safety. [PERSON] et al. (2020) [6,7] introduced a multi-channel tensor approach to model pedestrian environmental information, employing convolutional operations to capture spatiotemporal interactions. However, these methods predominantly rely on image-based data, which can be sensitive to noise and outliers, limiting their accuracy in dynamic, real-world environments.  
Despite the growing body of work in pedestrian safety, few studies have specifically addressed the prediction of pedestrian evasive behavior in school zones, a critical issue given the heightened risks associated with school commutes. Most existing research focuses on generalized traffic environments or utilizes traditional methods that lack consideration for key traffic variables. In contrast, this study offers a novel approach by integrating real-time video data with three predictive models: the traditional BP neural network, an improved GA-BP neural network, and the XGBoost ensemble learning method. While all three models are applied to predict pedestrian avoidance behavior, specifically in school zones, the GA-BP model demonstrates superior predictive accuracy and stability.  
Furthermore, this research considers key traffic variables such as vehicle speed, pedestrian proximity, and traffic density, providing a more context-aware and accurate prediction of pedestrian evasive actions. The GA-BP model, which improves learning efficiency and robustness compared to traditional BP neural networks, addresses the limitations of standard machine learning approaches in real-world, dynamic environments. This study improves the accuracy of pedestrian behavior prediction in school zones and provides valuable insights into the key factors influencing pedestrian safety.  
The structure of this paper is as follows: Section 2 presents the data collection methods. Section 3 introduces the Materials and Methods used in the study. Section 4 provides a comparison analysis of the prediction models. Finally, Section 5 concludes the study and discusses potential directions for future research.