Develop Improved Queue Warning System Combining Multiple Data Sources
| 项目名称： | Develop Improved Queue Warning System Combining Multiple Data Sources |
| 摘要： | Existing queue warning (QW) systems predominantly use infrastructure-based sensors to detect the formation of queues and use dynamic message signs (DMS) to warn drivers. These QW systems, may be inadequate where the required number or density of sensors are not available. Research has shown that crowd sourced probe and connected vehicle (CV) trajectory data used in combination with sensor data can significantly improve the accuracy and latency of queue detection. Furthermore, because of gaps between fixed location of DMSs a subset of drivers can encounter a queue without seeing any warning. Sharing queue information through third-party providers and providing vehicle-specific queue warning in CVs can ensure provision of queue warning to a broader audience in a timely manner. The research team will: (1) Develop detailed design of an enhanced queue detection and warning system that combine point-, probe-, and vehicle trajectory data. (2) Test and fine-tune the queue detection algorithm design using computer simulation. (3) Conduct a proof of concept, prototype deployment and field evaluation of the new QW system design on a freeway segment where vehicle queues frequently form. (4) Document the systems engineering and algorithm for the QW system as a potential enhancement to the Lonestar™ advanced traffic management system software application. |
| 状态： | Active |
| 资金： | 379729 |
| 资助组织： | Texas Department of Transportation |
| 管理组织： | Texas Department of Transportation |
| 项目负责人： | Pridgen, Shelley |
| 执行机构： | Texas A&M Transportation Institute |
| 主要研究人员： | Pesti, Geza |
| 开始时间： | 20220906 |
| 预计完成日期： | 20240831 |
| 主题领域： | Data and Information Technology；Highways；Operations and Traffic Management |
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