Enabling GLOSA through Domain Knowledge Aware SPAT Prediction and Queue Length Aware Trajectory Optimization

This study extends efforts in improving Signal Phase and Timing (SPAT) prediction and Trajectory Optimization near signalized intersections. It focuses on efficiently modeling and addressing uncertainties in intersections controlled by actuated traffic signals. One major uncertainty is using deep learning for SPAT prediction. While deep learning performs well most of the time, there are instances of faulty predictions. To address this, the study combines deep learning with traffic signal domain expertise to ensure accurate predictions aligned with traffic signal controller logic. In trajectory optimization, uncertainties arise from predicting the waiting queue and its clearance time at traffic signals. This task involves complex factors like traffic conditions, vehicle dynamics, and driver behavior, including perception reaction time. Incorporating queue length estimation and clearance time into the trajectory planning algorithm will enable fuel-efficient optimization, particularly beneficial for Green Light Optimal Speed Advisory (GLOSA) during high traffic demands when queues have a significant impact. This study will conduct a comprehensive literature review to assess the current state of SPAT prediction and queue estimation, considering relevant publications in traffic signal control, machine learning, and optimization. The aim is to identify optimal approaches for incorporating domain knowledge into SPAT prediction and integrating queue estimation into trajectory planning.

Language

• English

Project

• Status: Active
• Funding: $100000
• Contract Numbers:

  69A3552348303

• Sponsor Organizations:

  Sustainable Mobility and Accessibility Regional Transportation Equity Research Center

  Morgan State University
  Baltimore, MD  United States 

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Project Managers:

  Niehaus, Joseph

• Performing Organizations:

  Virginia Polytechnic Institute and State University, Blacksburg

  Virginia Tech Transportation Institute
  3500 Transportation Research Plaza
  Blacksburg, VA  United States  24061
• Principal Investigators:

  Rakha, Hesham

  Shafik, Amr

  Eteifa, Seifeldeen

• Start Date: 20230901
• Expected Completion Date: 20240901
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Machine learning; Optimization; Signalized intersections; Traffic queuing; Traffic signal timing; Vehicle trajectories
• Identifier Terms: Green Light Optimal Speed Advisory (GLOSA)
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01893875
• Record Type: Research project
• Source Agency: Sustainable Mobility and Accessibility Regional Transportation Equity Research Center
• Contract Numbers: 69A3552348303
• Files: UTC, RIP
• Created Date: Sep 21 2023 2:18PM