Machine Vision Toolkit for Automated Fleet Composition Assessment and Reporting

State Departments of Transportation (DOTs) and Metropolitan Planning Organizations (MPOs) employ fleet composition data (e.g., passenger vehicles, single-unit trucks, and combination trucks) in a variety of planning, economic, roadway performance, and safety applications. Accurate fleet composition data is essential for pavement management, safety analysis, and fuel consumption modeling. However, traditional methods are labor-intensive, costly, and often lack the temporal or spatial resolution required to capture variations between freeways, arterials, and managed lanes vs. general-purpose lanes. Using machine vision tools to quickly, efficiently, and accurately capture on-road percentages of light-duty vehicle, light-duty truck, medium-duty truck, and a variety of heavy-duty truck classifications will enhance analytical and modeling accuracy and reduce state DOT data management costs. Building upon prior National Center for Sustainable Transportation (NCST) research that developed machine vision algorithms for vehicle identification, this project will package those research findings into a deployable, open-source Automated Fleet Classification Toolkit for practitioners and researchers. The research team will develop and release comprehensive Standard Operating Procedures (SOPs) and software tools allowing agencies to convert standard roadside or overpass video feeds into high-resolution fleet composition data. The toolkit will utilize advanced object detection (e.g., YOLO architectures) to automate the identification of vehicle classes (aligning with FHWA 13-category schemes where possible) and propulsion types based on visual vehicle features. The system is designed to distinguish traffic conditions on complex roadway geometries, allowing users to generate separate classification profiles for managed lanes vs. general-purpose lanes, and separating freeway mainlines from adjacent arterial service roads. The project focuses on technology transfer: providing the "how-to" manuals, open-source code, and data processing protocols so that State DOTs, consultants, university partners and research institutes can replicate the data collection and extraction without relying on proprietary "black box" services.

• Record URL:
  • https://ncst.ucdavis.edu/project/machine-vision-toolkit-automated-fleet-composition-assessment-and-reporting

Language

• English

Project

• Status: Active
• Funding: $120,000.00
• Contract Numbers:

  DOT 69A3552348319

  DOT 69A3552344814

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  National Center for Sustainable Transportation

  University of California, Davis
  Davis, CA  United States 

  Georgia Institute of Technology, Atlanta

  790 Atlantic Drive
  Atlanta, GA  United States  30332-0355
• Managing Organizations:

  National Center for Sustainable Transportation

  University of California, Davis
  Davis, CA  United States 

  University of California, Davis

  1 Shields Ave
  Davis, California  United States  95616

  Georgia Institute of Technology, Atlanta

  790 Atlantic Drive
  Atlanta, GA  United States  30332-0355
• Project Managers:

  Cliff, Sydney

• Performing Organizations:

  National Center for Sustainable Transportation

  University of California, Davis
  Davis, CA  United States 

  Georgia Institute of Technology, Atlanta

  790 Atlantic Drive
  Atlanta, GA  United States  30332-0355
• Principal Investigators:

  Liu, Ziming

• Start Date: 20260401
• Expected Completion Date: 20270331
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Detection and identification systems; Fleet management; Machine vision; Vehicle classification; Vehicle fleets
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01985467
• Record Type: Research project
• Source Agency: National Center for Sustainable Transportation
• Contract Numbers: DOT 69A3552348319, DOT 69A3552344814
• Files: UTC, RIP
• Created Date: Apr 12 2026 11:29PM