  Annual Average Daily Traffic (AADT) is an important parameter used in traffic
engineering analysis. Departments of Transportation (DOTs) continually collect
traffic count using both permanent count stations (i.e., Automatic Traffic
Recorders or ATRs) and temporary short-term count stations. In South Carolina,
87% of the ATRs are located on interstates and arterial highways. For most
secondary highways (i.e., collectors and local roads), AADT is estimated based
on short-term counts. This paper develops AADT estimation models for different
roadway functional classes with two machine learning techniques: Artificial
Neural Network (ANN) and Support Vector Regression (SVR). The models aim to
predict AADT from short-term counts. The results are first compared against
each other to identify the best model. Then, the results of the best model are
compared against a regression method and factor-based method. The comparison
reveals the superiority of SVR for AADT estimation for different roadway
functional classes over all other methods. Among all developed models for
different functional roadway classes, the SVR-based model shows a minimum root
mean square error (RMSE) of 0.22 and a mean absolute percentage error (MAPE) of
11.3% for the interstate/expressway functional class. This model also shows a
higher R-squared value compared to the traditional factor-based model and
regression model. SVR models are validated for each roadway functional class
using the 2016 ATR data and selected short-term count data collected by the
South Carolina Department of Transportation (SCDOT). The validation results
show that the SVR-based AADT estimation models can be used by the SCDOT as a
reliable option to predict AADT from the short-term counts.
