Development and evaluation of an open-source, machine learning-based average annual daily traffic estimation software

TL;DR

This paper introduces an open-source machine learning software called 'estimAADTion' that improves the accuracy of AADT estimation from short-term traffic counts, outperforming traditional factor-based methods.

Contribution

The paper presents a novel open-source software utilizing support vector regression to estimate AADT more accurately from short-term counts, addressing limitations of existing factor-based models.

Findings

estAADTion achieves a 3% MAPE in AADT estimation

Factor-based method has a 6% MAPE

Software validated with South Carolina traffic data

Abstract

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%.