VISIAM (Visual Street Index for Active Mobility): An AI-Based Tool for Assessing Bikeability and Walkability of Streets

This project proposes the Visual Street Index for Active Mobility (VISIAM)—an AI-driven framework that integrates computer vision, self-supervised deep learning, and human-perception data to systematically assess bikeability and walkability at the street segment level. Current tools for assessing bikeability and walkability are limited because they rely on subjective audits or basic metrics. VISIAM addresses this gap through a multi-stage framework: computer vision models extract and classify streetscape features from Google Street View imagery, human-perception evaluations from diverse stakeholder groups rate street imagery across multiple dimensions, and the AI-derived classifications and human ratings are integrated to produce a composite bikeability and walkability score for each street segment. The final output is a citywide, street-level index visualized through interactive maps and dashboards, enabling policymakers to identify gaps, prioritize investments, and explore how infrastructure improvements could improve street quality.

• Record URL:
  • https://smartercenter.org/about-2/projects/visiam-visual-street-index-for-active-mobility/

Language

• English

Project

• Status: Active
• Funding: $239,850.00
• Contract Numbers:

  69A3552348303

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Safety and Mobility Advancements Regional Transportation and Economics Research Center

  Morgan State University
  Baltimore, MD  United States 
• Performing Organizations:

  Safety21 University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213
• Principal Investigators:

  Chavis, Celeste

  Frías-Martínez, Vanessa

• Start Date: 20260301
• Expected Completion Date: 20270901
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Artificial intelligence; Computer vision; Deep learning; Streetscape; Walkability
• Identifier Terms: Bikeability; Google Earth
• Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting;

Filing Info

• Accession Number: 01982070
• Record Type: Research project
• Source Agency: Sustainable Mobility and Accessibility Regional Transportation Equity Research Center
• Contract Numbers: 69A3552348303
• Files: UTC, RIP
• Created Date: Mar 11 2026 3:15PM