Contributing Factors of Annual Average Daily Traffic in a Florida County: Exploration with Geographic Information System and Regression Models

Abstract

Accurate annual average daily traffic (AADT) information is important to many applications, including roadway design, air quality compliance, travel model validation, and others. Yet complete or even extensive coverage of a network to collect traffic count data is impractical because of the cost involved. Studies have been conducted to estimate AADT for interstate highways, expressways, urban roads, and rural roads. Such efforts were often hampered by the lack of relevant detailed information that adequately explained variations in the traffic counts. A study in which geographic information system technology was extensively used to investigate various factors that may contribute to AADT on a road is presented. A variety of land-use and accessibility measurements were developed and tested. Multiple linear regression models were developed. Although most variables were statistically significant, few added enough explanatory power to be practical and useful. Four models that achieved R² of 0.66 to 0.82 are presented. The predictive power of the models are also examined and compared.