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Traditionally, Departments of Transportation (DOTs) use the factor-basedmodel to estimate Annual Average Daily Traffic (AADT) from short-term trafficcounts. The expansion factors, derived from the permanent traffic countstations, are applied to the short-term counts for AADT estimation. Theinherent challenges of the factor-based method (i.e., grouping the countstations, applying proper expansion factors) make the estimated AADT valueserroneous. Based on a survey conducted by the authors, 97% of the 39 publictransportation agencies use the factor-based AADT estimation model, and theseagencies face the aforementioned challenges while using factor-based models toestimate AADT. To derive a more accurate AADT, this paper presents the"estimAADTion" software, which is an open-source software developed based on amachine learning method called support vector regression (SVR) for estimatingAADT using 24-hour short-term count data. DOTs conduct short-term counts atdifferent locations periodically. This software has been designed to estimateAADT at a particular location from the short-term counts collected at thoselocations. In order to estimate AADT from short-term counts, the software usesdata from permanent count stations to train the SVR model. The performance ofthe "estimAADTion" software is validated using the short-term count data fromSouth Carolina. The Mean Absolute Percentage Error (MAPE) of the AADT estimatedfrom the software is 3%, while the factor-based method produces a MAPE value of6%.

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