A Bayesian analysis of the effect of estimating annual average daily traffic for heavy-duty trucks using training and validation data-sets

Abstract

The precise estimation of annual average daily traffic (AADT) is of significant importance worldwide for transportation agencies. This paper uses three modeling frameworks to predict the AADT for heavy-duty trucks. In total, 12 models are developed based on regression and Bayesian analysis using a training data-set. A separate validation data-set is used to compare the results from the 12 models, spanning the years 2005 through 2007 and taken from 67 continuous data recorders. Parameters of significance include roadway functional class, population density, and spatial location; five regional areas - northeast, northwest, central, southeast, and southwest - of the state of Ohio in the USA; and average daily truck traffic. The results show that a full Bayesian negative binomial model with a coefficient offset is the most efficient model framework for all four seasons of the year. This model is able to account for between 87% and 92% of the variability within the data-set. © 2013 Copyright Taylor and Francis Group, LLC.