Estimating Average Daily Traffic on Low-Volume Roadways in Louisiana

Abstract

Traffic volume is an important parameter that agencies use as a decisive factor, especially at the time of design, maintenance, and operation of roadways. Thus, its correct estimation is very essential. There are already a few established proprietary products available on the market that can predict traffic volumes. In addition, past studies have used several mathematical models to predict traffic volume. Relatively fewer studies have been conducted on predicting traffic volume on low-volume roadways. To bridge the gap on the prediction of traffic volume for low- and high-volume roadways, this study seeks to find practical, cost-effective, and progressive methods of estimating and classifying traffic on low-volume rural roadways. Across the state of Louisiana, 395 locations with low traffic volumes of less than 500 vehicles per day were selected. Census tract data was used to extract demographic and socioeconomic information for each location. Two prediction models—linear regression and random forest regression models—were developed to predict traffic volumes on these low-volume roadways. The results showed that the linear regression model had the highest predictive accuracy, with R-square of 0.979 and root mean square error of 70.26 compared with the RMSE of 110.23 for the random forest regression model. Both models found functional class, land use, number of lanes, population density, median age, median household income, and household density significantly affecting the traffic volume.