Development of Statewide AADT Estimation Model from Short-Term Counts: A Comparative Study for South Carolina

TL;DR

This study develops and compares machine learning models, specifically SVR and ANN, to estimate AADT from short-term counts in South Carolina, finding SVR to be the most accurate and reliable method across different roadway classes.

Contribution

The paper introduces SVR and ANN models for AADT estimation and demonstrates SVR's superior accuracy over traditional methods in South Carolina.

Findings

SVR outperforms ANN and traditional methods in AADT estimation.

SVR models achieve low RMSE and MAPE values, indicating high accuracy.

Validated models are reliable for use by SCDOT for traffic planning.

Abstract

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.