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

Term Start:

June 1, 2026

Term End:

May 31, 2027

Budget:

$220,776

Keywords:

Freight traffic, Machine Learning, Travel Behavior

Thrust Area(s):

Data Collection Mechanisms, Data Modeling and Analytic Tools

University Lead:

Georgia Institute of Technology

Researcher(s):

Sofia Perez-Guzman

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.

Project scope

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