Intelligent CCTV for Urban Design: AI-Based Analysis of Soft Infrastructure at Intersections
For urban planners and traffic engineers, this provides a low-cost, rapid method to evaluate temporary traffic calming measures using existing infrastructure.
This study uses AI and computer vision on existing CCTV footage to evaluate soft infrastructure interventions (pedestrian refuges, curb extensions) at intersections in Minneapolis, finding mean speed reductions up to 20% and pass-through traffic decreases up to 12.2%.
Artificial intelligence (AI) and computer vision are transforming transportation data collection. This study introduces an AI-enabled analytics framework leveraging existing CCTV infrastructure to evaluate the impact of soft interventions, such as temporary pedestrian refuges and curb extensions, on vehicle speed and safety. Using deep learning and perspective-based speed estimation, we evaluated driver behavior before and after interventions, with repeated post-installation monitoring in Week 1 and Week 2, in Minneapolis. Findings reveal that at unsignalized intersections, mean and 85th-percentile speeds fell by up to 18.75% and 16.56%, respectively, while pass-through traffic decreased by as much as 12.2%. Signalized intersections showed comparable reductions except one location, with mean and 85th-percentile speeds dropping by up to 20.0% and 17.19%. These results demonstrate the traffic-calming effectiveness of soft infrastructure and underscore the utility of AI-powered methods for rapid, low-cost, and evidence-based transport policy evaluation.