Crosswalk Change

Research on crosswalk change focuses on improving pedestrian safety and optimizing infrastructure management through advancements in detection, prediction, and understanding of pedestrian behavior at crosswalks. Current efforts utilize computer vision techniques, including convolutional neural networks and automated GIS-based frameworks analyzing high-resolution imagery, to detect crosswalk modifications and automatically trigger signals based on pedestrian presence. These studies also investigate how cultural factors and human-vehicle interactions influence pedestrian crossing decisions, particularly in the context of autonomous vehicles, aiming to improve the safety and efficiency of pedestrian crossings. The findings inform the development of smarter, safer crosswalks and contribute to improved traffic management and urban planning.