Estimating switching times of Actuated Coordinated Traffic Signals: A deep learning approach
| 项目名称： | Estimating switching times of Actuated Coordinated Traffic Signals: A deep learning approach |
| 摘要： | Acceleration and Deceleration at signalized intersections are a major hindrance to vehicle fuel efficient operations. Green Light optimal speed advisory (GLOSA) allows controlling vehicles in a fuel-efficient manner but requires reliable estimates of signal switching time. This study aims at utilizing data from actuated coordinated signalized intersections in North Virginia along with multiple deep learning and machine learning techniques to provide estimates of traffic signal switching times from green to red and vice versa. These estimates can be used to enable more fuel-efficient operation using GLOSA and eco-driving. They can also be used to mitigate dilemma zone safety concerns. A comparative analysis will be conducted between the different techniques used and their pros and cons in terms of prediction errors and robustness to different traffic conditions. |
| 状态： | Active |
| 资金： | 60000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 执行机构： | Virginia Polytechnic Institute and State University, Blacksburg |
| 开始时间： | 20201001 |
| 预计完成日期： | 20211231 |
| 主题领域： | Highways；Operations and Traffic Management；Planning and Forecasting |
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