Machine Learning-Driven Pedestrian Recognition and Behavior Prediction for Enhancing Public Safety in Smart Cities Authors

Yuan Sun, Jorge Ortiz

Abstract: The study presents the development and implementation of pedestrian recognition and behavior prediction technologies within smart city infrastructure, focusing on enhancing traffic management and public safety. By integrating real-time data from sensors, LIDAR, and cameras, the system leverages advanced machine learning models, including Long Short-Term Memory (LSTM) and Transformer architectures, to predict pedestrian movements with 93% accuracy. The predictive model was deployed in a simulated urban environment, leading to a 20% reduction in vehicle idle time and a 15% increase in average vehicle speed, thereby optimizing traffic flow. Furthermore, the integration of Vehicle-to-Everything (V2X) communication and 5G technology enabled real-time interaction between vehicles, pedestrians, and traffic control systems. The system effectively reduced near-miss incidents by 30% and provided an average reaction time of 1.8 seconds for vehicles in hazardous pedestrian scenarios. Additionally, the model identified 87% of potential pedestrian hazards, significantly improving public safety. Despite these advancements, challenges such as data privacy concerns and hardware limitations in large-scale deployments remain. Future research will focus on overcoming these challenges through multi-modal data fusion and the development of real-time learning algorithms, making smart cities more adaptive and efficient.