1 research outputs found
Development and evaluation of an open-source, machine learning-based average annual daily traffic estimation software
Traditionally, Departments of Transportation (DOTs) use the factor-based
model to estimate Annual Average Daily Traffic (AADT) from short-term traffic
counts. The expansion factors, derived from the permanent traffic count
stations, are applied to the short-term counts for AADT estimation. The
inherent challenges of the factor-based method (i.e., grouping the count
stations, applying proper expansion factors) make the estimated AADT values
erroneous. Based on a survey conducted by the authors, 97% of the 39 public
transportation agencies use the factor-based AADT estimation model, and these
agencies face the aforementioned challenges while using factor-based models to
estimate AADT. To derive a more accurate AADT, this paper presents the
"estimAADTion" software, which is an open-source software developed based on a
machine learning method called support vector regression (SVR) for estimating
AADT using 24-hour short-term count data. DOTs conduct short-term counts at
different locations periodically. This software has been designed to estimate
AADT at a particular location from the short-term counts collected at those
locations. In order to estimate AADT from short-term counts, the software uses
data from permanent count stations to train the SVR model. The performance of
the "estimAADTion" software is validated using the short-term count data from
South Carolina. The Mean Absolute Percentage Error (MAPE) of the AADT estimated
from the software is 3%, while the factor-based method produces a MAPE value of
6%.Comment: 16 Pages, 6 Figures, 1 Tabl