Machine Learning-Driven Pedestrian Recognition and Behavior Prediction for Enhancing Public Safety in Smart Cities - Sun, Yuan; Ortiz, Jorge (Journal of Artificial Intelligence and Information, 2024)
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.