摘要：Average Annual Daily Traffic is typically estimated by applying seasonal factors (SFs) to short-term counts. SFs are obtained from continuous count sites and assigned to short-term count sites. This assignment procedure is usually empirical and subjective. Some previous studies have attempted to establish relationships between SFs and influential variables to provide an objective and data-driven alternative for SF assignment. However, in rural areas, SFs are difficult to model due to low land use intensity and, sometimes, significant through traffic. This paper presents a study of relationships between monthly SFs and hourly traffic patterns, land use, and other variables, using data from 116 continuous counters in rural areas throughout Florida. It is found that hourly traffic patterns are related to traffic seasonality and can be used to improve the modeling of influential variables that affect SF. The influential variables are then used for seasonal factor assignment and estimation. The proposed method achieved an average error of four percent, with 95 percent of the estimated monthly SFs having an error of no more than ten percent.
