Estimating Traffic Stream Density Using Connected Vehicle Data

The number of on-road vehicles has increased rapidly over the past few decades, leading to serious traffic congestion in many areas. An efficient way of solving traffic congestion is improving traffic management strategies using advanced technologies and advanced traffic signal control systems that optimize traffic signal timings in real-time. Knowing the number of vehicles on a specific roadway segment is crucial in developing efficient adaptive traffic signal controllers; however, it is difficult to measure traffic density directly in the field. This research aims to estimate the total number of vehicles on signalized approaches using only connected vehicle (CV) data. The estimate outcomes can be provided to traffic signal controllers to optimally determine the allocation of green time for each traffic signal phase, leading to better intersection performance measures. Different estimators (filters) using CV data will be developed to estimate the total number of vehicles on signalized links, such as Kalman and particle filters. One concern with using CVs is measuring their level of market penetration (LMP). The LMP is defined as the ratio of the total number of CVs to the total number of vehicles. Providing accurate LMP estimates should improve the estimation accuracy of the vehicle counts. Therefore, in this research, a machine-learning model will be developed to provide real-time estimates of the LMP values. Then, the developed filtering model will be combined with the developed machine learning model to improve the vehicles count estimation accuracy. In addition, an adaptive filtering technique will be developed to enable real-time estimates of statistical parameters of the system noise rather than using predefined values for the entire simulation. Finally, this research will examine the impacts of traffic demand level on the estimation model, considering both under- and over-saturated conditions.

Language

  • English

Project

  • Status: Active
  • Funding: $150000
  • 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

  • Start Date: 20200501
  • Expected Completion Date: 20210430
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01741864
  • Record Type: Research project
  • Source Agency: Urban Mobility & Equity Center
  • Contract Numbers: 69A43551747123
  • Files: UTC, RiP
  • Created Date: Jun 3 2020 11:52AM