Develop Improved Queue Warning System Combining Multiple Data Sources

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.

• Record URL:
  https://library.ctr.utexas.edu/Presto/project=7140

Language

• English

Project

• Status: Completed
• Funding: $379729
• Contract Numbers:

  0-7140

• Sponsor Organizations:

  Texas Department of Transportation

  125 E. 11th Street
  Austin, TX  United States  78701-2483
• Managing Organizations:

  Texas Department of Transportation

  125 E. 11th Street
  Austin, TX  United States  78701-2483
• Project Managers:

  Odell, Wade

• Performing Organizations:

  Texas A&M Transportation Institute

  Texas A&M University System
  3135 TAMU
  College Station, TX  United States  77843-3135
• Principal Investigators:

  Pesti, Geza

• Start Date: 20220906
• Expected Completion Date: 20240831
• Actual Completion Date: 20240831

Subject/Index Terms

• TRT Terms: Connected vehicles; Data fusion; Probe vehicles; Traffic queuing; Variable message signs; Vehicle trajectories; Warning systems
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01863336
• Record Type: Research project
• Source Agency: Texas Department of Transportation
• Contract Numbers: 0-7140
• Files: RIP, STATEDOT
• Created Date: Nov 3 2022 2:33PM