Alleviating Traffic Congestion: Developing and Evaluating Novel Multi-Agent Reinforcement Learning Traffic Light Coordination Techniques
| 项目名称： | Alleviating Traffic Congestion: Developing and Evaluating Novel Multi-Agent Reinforcement Learning Traffic Light Coordination Techniques |
| 摘要： | Traffic congestion costs American cities tens of billions of dollars per year, not to mention its negative impact on the environment or people’s mental health. Novel Markov game models and advanced reinforcement learning algorithms hold the promise of drastically alleviating congestion through dynamic coordination of traffic signals and adaptive techniques to dynamically re-route traffic. This project involves a collaboration with Econolite, a leading provider of traffic management systems. |
| 状态： | Active |
| 资金： | 200000 |
| 资助组织： | Carnegie Mellon University；Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Carnegie Mellon University |
| 项目负责人： | Kline, Robin |
| 执行机构： | Carnegie Mellon University |
| 主要研究人员： | Fang, Fei |
| 开始时间： | 20220701 |
| 预计完成日期： | 20230630 |
| 主题领域： | Highways；Operations and Traffic Management；Planning and Forecasting |
相关文献
- Multi-scale and Collaborative Disaster Evacuation Planning Framework
- Developing an Open Source Multi-Agent Simulation Environment for Connected Autonomous Vehicles (CAVs)
- Eco-Driving of Connected and Autonomous Vehicles Approaching and Departing Signalized Intersections
- Evaluation of the Impacts of Enhancing Public Transportation Systems by Integrating Shared Mobility for First- and Last-Mile Transit Access
- One-to-Many Simulator Interface with Virtual Test Bed for Equitable Tech Transfer
- Safe Reinforcement Learning for Intersection Management in RITI Communities Under Rare Extreme Events
检索历史
应用推荐
