• Develop Local Functional Classification VMT and AADT Estimation Method

    NCDOT Research Project Number: 2019-12

 Executive Summary

  • 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 the proposed research project are: 1) to review 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.

Srinivas Pulugurtha
Srinivas Pulugurtha
Kent Taylor
Lisa E. Penny
UNC Chapel Hill

 Related Documents

 Report Period

  • August 1, 2018 - May 31, 2020


  • Complete


  • Planning, Policy, Programming and Multi-modal

 Sub Category

  • Traffic Surveys, Modeling and Forecasting

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