Date of Award

Fall 2020

Document Type

Open Access Dissertation


Civil and Environmental Engineering

First Advisor

Nathan Huynh


Data from the Federal Highway Administration (FHWA) indicates that there is an increase in the number of vehicles on already congested roadways. For a department of transportation (DOT) to keep up with this increased demand, it is necessary for them to continuously collect and monitor traffic volume on the roads they maintain. One of the most important parameters that DOTs collect and use for traffic engineering and transportation planning studies is annual average daily traffic (AADT). The DOTs are also required to collect and report AADTs to the Federal Highway Administration annually as part of the Highway Performance Monitoring System (HPMS) program. AADTs are typically obtained by using pneumatic tubes to count traffic for 24 hours; these “short-term” counts are then converted to AADTs based on expansion factors. This method requires an enormous amount of time and money. For these reasons, the SCDOT can only afford to perform short-term counts at a limited number of locations throughout the state every two or three years. The counts from these locations are known as “coverage counts”. However, the South Carolina DOT (SCDOT) is required to determine and report the AADTs on all roads it maintains, including non-coverage locations, where short-term counts have never been collected or were collected more than 10 years ago. In absence of a methodology, the SCDOT simply assumes the AADT to be 100 vehicles/day (vpd) for a rural local road and 200 vpd for an urban local road. This thesis investigates the applicability and effectiveness of the kriging method to estimate AADT at non-coverage locations. Other studies have investigated the use of kriging to estimate AADTs, but they have been applied at a local or regional level. This study was the first to evaluate the kriging method statewide. The effectiveness of the kriging method was evaluated against other interpolation methods, including nearest neighbor, average k nearest neighbors, inverse distance weighting, and the SCDOT’s current default values.