Reinforcement Learning for Optimal Speed Limit Control Over Network
| 项目名称： | Reinforcement Learning for Optimal Speed Limit Control Over Network |
| 摘要： | 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. |
| 状态： | Active |
| 资金： | 35000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 执行机构： | University of Central Florida, Orlando |
| 开始时间： | 20210808 |
| 预计完成日期： | 20230207 |
| 主题领域： | Highways；Operations and Traffic Management；Safety and Human Factors；Vehicles and Equipment |
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