Using Big Data and Machine Learning to Evaluate and Optimize the Performance of Traffic Signals in Tennessee

In the state of Tennessee, thousands of traffic signals covering major metropolitan areas and dotting smaller cities and major arterials are an integral part of the State’s transportation mobility system. Most traffic signals utilize fixed-cycle scheduling and there are no continuing, systematic, and standardized approaches to evaluating and improving their performance; instead, a very small number of traffic signal timing plans are reviewed by consultants only after repeated complaints or unusual crash numbers. This is highly undesirable and has not been addressed so far due to cost, manpower, jurisdiction, and technology issues. This project will address this problem by finding an economical way to evaluate the performance of the traffic signals in Tennessee and identify the most deficient ones that need immediate attention.

Language

  • English

Project

  • Status: Active
  • Funding: $ 104707
  • Contract Numbers:

    RES2021-09

  • Sponsor Organizations:

    Tennessee Department of Transportation

    James K. Polk Building
    Fifth and Deaderick Street
    Nashville, TN  United States  37243-0349
  • Project Managers:

    Bryan, Stephen

  • Performing Organizations:

    Middle Tennessee State University, Murfreesboro

    1301 East Main Street
    Murfreesboro, TN  United States  37132-0001
  • Principal Investigators:

    Miao, Lei

  • Start Date: 20200901
  • Expected Completion Date: 20220228
  • Actual Completion Date: 0
  • USDOT Program: Transportation, Planning, Research, and Development

Subject/Index Terms

Filing Info

  • Accession Number: 01754305
  • Record Type: Research project
  • Source Agency: Tennessee Department of Transportation
  • Contract Numbers: RES2021-09
  • Files: RiP, STATEDOT
  • Created Date: Oct 7 2020 12:07PM