Developing an Intelligent Connected Vehicle based Traffic State Estimator

Urban cities are growing and transport infrastructure is being hampered, resulting in congestion, delays, safety problems and increased fuel consumption. One proposed solution is intelligent transport systems that lead to better management of roads and improvements in traffic conditions. Measuring the total number of vehicles approaching an intersection is crucial for the traffic signal performance. Efficient adaptive traffic controls can be developed once accurate measurements are estimated. In this research, various estimators will be developed using the connected vehicle (CV) data to estimate the total number of vehicles on multi-lane links. Measuring the level of market penetration (LMP) is one of the main concerns for CVs use. By providing accurate LMP estimates, the accuracy of vehicle count estimates should be improved. Therefore, a deep learning model will be developed to provide the LMP values in real time. The developed estimator will then be integrated with the deep learning model developed to improve the accuracy of the vehicle estimates. This research will further study the impacts of traffic demand level, vehicles type, and initial conditions on the performance of the developed estimators.

Language

• English

Project

• Status: Completed
• Funding: $80000
• Contract Numbers:

  69A43551747123

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Urban Mobility & Equity Center

  Morgan State University
  Baltimore, MD  United States  21251
• 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

  Abdelrahman, Ahmed

  Abdelghaffar, Hossam

• Start Date: 20210101
• Expected Completion Date: 20230630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Connected vehicles; Estimating; Machine learning; Market penetration; Traffic counting
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01762353
• Record Type: Research project
• Source Agency: Urban Mobility & Equity Center
• Contract Numbers: 69A43551747123
• Files: UTC, RIP
• Created Date: Jan 19 2021 8:42PM