Develop Local Functional Classification VMT and AADT Estimation Method

Rapid growth in population over the past two decades has led to an increase in travel demand, resulting in congestion and an exponential increase in conflicts that arise because of human interaction, off- and on network characteristics, and other associated factors. To better cater the increase in demand and reduce congestion, a federally-funded, state-administered program known as Highway Safety Implementation Program (HSIP) is legislated. The goal of HSIP is to achieve a significant reduction in fatalities and serious injuries on public roads. One of the requirements of HSIP for state agencies is to report Annual Average Daily Traffic (AADT) on all paved public roads (includes functionally classified major and local roads) and develop safety performance measures. A significant amount of resources (time and money) are spent by agencies to collect AADT on these road links. However, resource constraints limit agencies from collecting AADT data for all the links, particularly local functionally classified public roads. Such limitations can be offset using robust models that help estimate AADT on functionally classified major and local roads. The objectives of this research project are 1) to review annual average daily traffic (AADT) and vehicle miles traveled (VMT) generation methods, 2) to survey how other state departments of transportation are meeting the Highway Safety Improvement Program (HSIP) AADT requirements, 3) to develop models to estimate AADT on local roads, 4) to validate and calibrate the models to improve their predictability, and, 5) to recommend growth factors for continuously estimating AADT and VMT on local roads. The count-based AADT at 12,899 traffic count stations on local roads in North Carolina were used to develop and validate statistical and geospatial models. The influence of road, socioeconomic, demographic, and land use characteristics was examined. The outputs from statewide models were compared with the outputs from county-level models. An error analysis was performed to identify factors influencing the predictability of these models. Sample sizes and growth factors were computed for each county. Recommendations were made to estimate AADT and VMT based on the count-based AADT at traffic count stations, model outputs, and growth factors for the reporting year.


  • English


  • Status: Completed
  • Funding: $165931
  • Contract Numbers:


  • Sponsor Organizations:

    North Carolina Department of Transportation

    Research and Development
    1549 Mail Service Center
    Raleigh, NC  United States  27699-1549
  • Managing Organizations:

    North Carolina Department of Transportation

    Research and Development
    1549 Mail Service Center
    Raleigh, NC  United States  27699-1549
  • Project Managers:

    Penny, Lisa

  • Performing Organizations:

    University of North Carolina - Charlotte

    9201 University City Blvd
    Charlotte, North Carolina  United States  28223-0001
  • Principal Investigators:

    Pulugurtha, Srinivas

  • Start Date: 20180801
  • Expected Completion Date: 20200531
  • Actual Completion Date: 20200531

Subject/Index Terms

Filing Info

  • Accession Number: 01683726
  • Record Type: Research project
  • Source Agency: North Carolina Department of Transportation
  • Contract Numbers: FHWA/NC/2019-12
  • Files: RIP, STATEDOT
  • Created Date: Oct 19 2018 4:54PM