Enhanced Network Models for Multimodal Resiliency
This project will develop next-generation multimodal network resilience models. The research team will examine the performance of networked transportation systems in disrupted conditions using field data, generate mathematical models to describe system performance and user behavior, and develop mitigation strategies based on this model. This project is distinguished from previous efforts in this space by explicitly considering multiple travel modes. The team specifically will consider road transport, freight waterways, and port terminals as the transportation systems in their scope. The primary disruption the team will consider is a natural disaster, such as a hurricane, which can disrupt both transportation supply (closing roads, damaging port or waterway infrastructure) and demand (evacuation, closed businesses).
- Record URL:
Language
- English
Project
- Status: Active
- Funding: $120000
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Contract Numbers:
69A3552344815
69A3552348320
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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:
Center for Understanding Future of Travel Behavior and Demand
University of Texas
Austin, TX United States -
Project Managers:
Bhat, Chandra
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Performing Organizations:
University of Texas at Austin
Austin, TX United States 78712 -
Principal Investigators:
Boyles, Stephen
- Start Date: 20230901
- Expected Completion Date: 20250531
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Disaster resilience; Multimodal transportation; Network analysis (Planning); Service disruption; Transportation planning; Travel demand
- Subject Areas: Planning and Forecasting; Security and Emergencies; Transportation (General);
Filing Info
- Accession Number: 01917647
- Record Type: Research project
- Source Agency: Data-Supported Transportation Operations and Planning Center
- Contract Numbers: 69A3552344815, 69A3552348320
- Files: UTC, RIP
- Created Date: May 6 2024 4:23PM