Intelligent Measurement of Wheelchair Accessibility in Pedestrian Networks
A large-scale measurement method combining street-view evidence, pedestrian networks, and AI-enhanced visual analytics.
Abstract
This research develops a large-scale approach to measuring wheelchair accessibility in slow-traffic and pedestrian networks. It combines street-view imagery, spatial network analysis, computer vision, and dual-perspective visual analytics to reveal where mobility barriers accumulate and how they shape unequal access across Chinese megacities. The work aims to connect fine-grained environmental evidence with city-scale planning decisions.
Street-level evidence is connected to metropolitan pedestrian networks.