Characterizing Operational Disruptions in Urban Freight Networks: Patterns, Propagation, and Recovery Behaviors in Atlanta

Urban freight systems are subject to recurring disruptions that can degrade travel time reliability and operational efficiency. Despite their cumulative effects on freight movement and supply chain performance, these disruptions remain relatively understudied at the network level, particularly when only publicly funded and openly accessible data are used. This project develops a data-driven analytical framework to detect, characterize, attribute, and model recurring freight-relevant disruptions and assess their impacts on urban network performance. The research combines truck travel times, vehicle counts and classifications, congestion imagery, crash records, and street-network data to create an integrated representation of freight system conditions. Machine learning methods are used to detect and classify disruption events, while graph-based analysis and survival modeling examine their propagation and recovery dynamics. The resulting analyses distinguish disruptions that may be amenable to public-sector intervention from those that are largely exogenous and translate these patterns into freight-relevant performance measures. A proof-of-concept analytical prototype will demonstrate how the methods can support freight system assessment and performance-based planning. The Atlanta metropolitan area serves as the testbed, and the methods are designed to transfer to other regions using comparable public data.

Language

• English

Project

• Status: Active
• Funding: $220,776.00
• 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:

  Georgia Institute of Technology, Atlanta

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

  Perez-Guzman, Sofia

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

Subject/Index Terms

• TRT Terms: Freight transportation; Machine learning; Network analysis; Service disruption; Traffic congestion; Truck traffic; Urban goods movement
• Geographic Terms: Atlanta (Georgia)
• Subject Areas: Freight Transportation; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 02002865
• 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 18 2026 10:12AM