Beyond Pedestrian Flow: Evaluating Urban Sidewalk Friction and Accessibility via Efficient State Space Models

Traditional sidewalk performance evaluation has primarily relied on flow metrics that treat pedestrians as uniform units. These methods typically rely on basic pedestrian counts which capture pedestrians at a single point location. Walkability metrics typically assess the suitability of a street environment for pedestrian activity based on the presence or absence of specific neighborhood or street characteristics. While useful to capture high-level neighborhood pedestrian activity, these approaches fail to capture the complex, micro-level dynamics that occur in pedestrian spaces. To address this gap, this project introduces a comprehensive framework for evaluating sidewalk performance by developing novel friction metrics. By moving beyond simple counting, the research team aims to quantify how different pedestrian groups and infrastructural elements interact in real-world scenarios. The architecture leverages the combined strengths of Visual Language Models (VLMs) and highly efficient State Space Models (SSMs). The project is structured around three primary objectives: (1) Semantic Categorization via Visual Language Models — processing CCTV camera feeds to categorize pedestrians into distinct behavioral groups and identify infrastructural hurdles; (2) Development of SSM-Based Friction Metrics — using the semantic data generated by the VLMs, utilizing SSMs operating on anomaly detection principles to analyze specific pedestrian interactions over time; and (3) Walkability and Accessibility Evaluation — synthesizing the outputs of the pipeline to evaluate large-scale urban walkability and accessibility by creating and utilizing benchmark data.

Language

• English

Project

• Status: Active
• Funding: $200,000.00
• Contract Numbers:

  69A3552344815

  69A3552348320

• 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:

  University of Texas at Austin

  Austin, TX  United States  78712
• Performing Organizations:

  City College of New York

  Civil Engineering, Steinman T-127
  140th Street and Convent Avenue
  New York, NY  United States  10031
• Principal Investigators:

  Zhu, Zhigang

• Start Date: 20260601
• Expected Completion Date: 20270531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Accessibility; Friction; Machine learning; Pedestrian movement; Quantitative metrics; Sidewalks; Urban areas; Walkability
• Subject Areas: Data and Information Technology; Pavements; Pedestrians and Bicyclists; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 02001770
• Record Type: Research project
• Source Agency: Center for Understanding Future of Travel Behavior and Demand
• Contract Numbers: 69A3552344815, 69A3552348320
• Files: UTC, RIP
• Created Date: Sep 4 2026 3:18PM