Reinforcement Learning for Optimal Speed Limit Control Over Network
The goal is to optimize variable speed limit control (VSLC) strategies over network to improve both traffic safety and mobility. The investigators propose to use graph-based deep reinforcement learning to improve the control effectiveness and scalability. The proposed research will advance the current knowledge and practice of VSLC in two aspects. First, this research will enlarge the scope of VSLC from link-based to network-based control to bring a new understanding about its system-level safety implications. Second, it will optimize the impact of VSLC using multi-objective learning approaches considering both safety and mobility.
Language
- English
Project
- Status: Active
- Funding: $35000
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Contract Numbers:
69A3551747131
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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:
University of Iowa, Iowa City
National Advanced Driving Simulator, 2401 Oakdale Blvd
Iowa City, IA United States 52242-5003 -
Performing Organizations:
University of Central Florida, Orlando
Department of Civil, Environmental & Contruction Engineering
1280 Pegasus Drive, 442B Engineering II
Orlando, FL United States 32816 -
Principal Investigators:
Guo, Zhaomiao
- Start Date: 20210808
- Expected Completion Date: 20230207
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Machine learning; Mobility; Networks; Optimization; Traffic safety; Variable speed limits
- Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01778677
- Record Type: Research project
- Source Agency: Safety Research Using Simulation University Transportation Center (SaferSim)
- Contract Numbers: 69A3551747131
- Files: UTC, RIP
- Created Date: Aug 4 2021 2:49PM