Intelligent CCTV for Urban Design: AI-Based Analysis of Soft Infrastructure at Intersections

TL;DR

This paper presents an AI-based framework using CCTV footage to evaluate the impact of soft infrastructure on vehicle speed and safety at intersections, demonstrating significant traffic calming effects.

Contribution

The study introduces a novel AI-enabled analysis method leveraging existing CCTV infrastructure for rapid, low-cost evaluation of soft infrastructure interventions in urban transport.

Findings

Vehicle speeds decreased by up to 20% after interventions.

Pass-through traffic reduced by up to 12.2%.

AI methods proved effective for evidence-based policy evaluation.

Abstract

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.