This map visualizes the findings of our research paper (in review). We used machine learning to analyze over 7 million road segments across 100 cities, revealing a new typology of four readiness profiles for Connected and Automated Vehicles (CAVs).
A key finding is that 87% of urban roads offer minimal infrastructural support, placing the primary burden of safety on vehicle sensors.
This platform serves as an objective tool for policymakers, planners, and engineers to guide targeted investment, develop realistic deployment strategies, and inform evidence-based policy for a safe transition to automated mobility.
Data retrieved from OpenStreetMap (OSM) between March and April 2025.
Developed by Matheus Gomes Correia
