Research on Pedestrian Avoidance Behavior for School Section Based on Improved BP Neural Network and XGboost Algorithm - Guiliang Lu; Mingwei Liu (Applied Sciences)
As society evolves and technology advances, increasing transportation demands have heightened safety risks near schools and on mixed-traffic roads. While traditional studies on pedestrian evasive behavior have mainly focused on general traffic environments and used image-based features to predict trajectories, few have specifically addressed the behavior of pedestrians in school zones. This study fills that gap by analyzing pedestrian evasive actions near school zones in Pudong New Area, Shanghai, using real-time video data. In contrast to previous approaches, our research leverages key traffic variables—such as vehicle speed, pedestrian proximity, and traffic density—to predict whether pedestrians will engage in evasive behavior. We independently apply three predictive models: the traditional BP (Backpropagation) neural network, an improved GA-BP(genetic algorithm–backpropagation) neural network, and the XGBoost (Extreme Gradient Boosting) ensemble learning method. Our findings show that the improved GA-BP model outperforms the others, achieving an accuracy of over 79%. Furthermore, this study identifies crucial traffic factors influencing pedestrian behavior, offering valuable insights for road safety decision-making in school zones. This research demonstrates the potential of advanced predictive models for forecasting pedestrian evasive behavior. It enhances safety in school zones by highlighting the key traffic variables affecting pedestrians.