Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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%.

29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org