A data-driven framework for traffic incident duration prediction
Traffic incidents pose significant challenges to the efficient flow of transportation systems, causing congestion, delays, and potential safety hazards. This research aims to utilize several data sources, including probe vehicle data, to predict traffic recovery time and analyze the impact of each traffic duration component on traffic recovery time in the State of Maryland. The results derived from the traffic recovery time prediction models can be a valuable tool for decision-makers in planning alternative routes, adjusting signal timings, or providing real-time traffic information to drivers.
Language
- English
Project
- Status: Active
- Funding: $150000
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Contract Numbers:
CMMM-UMDAH-2023-C0001
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Sponsor Organizations:
University of Maryland, College Park
College Park, MD United States 20742Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of Maryland, College Park
College Park, MD United States 20742 -
Project Managers:
Haghani, Ali
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Performing Organizations:
University of Maryland, College Park
College Park, MD United States 20742 -
Principal Investigators:
Haghani, Ali
- Start Date: 20230901
- Expected Completion Date: 20240831
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Data analysis; Predictive models; Probe vehicles; Time duration; Traffic congestion; Traffic incidents
- Geographic Terms: Maryland
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01909255
- Record Type: Research project
- Source Agency: Center for Multi-Modal Mobility in Urban, Rural, and Tribal Areas (CMMM)
- Contract Numbers: CMMM-UMDAH-2023-C0001
- Files: UTC, RIP
- Created Date: Feb 22 2024 3:46PM