A Methodology to Characterize Ideal Short-term Counting Conditions and Improve AADT Estimation Accuracy Using a Regression-based Correcting Function

Transportation agencies’ motor vehicle count programs tend to be well-established and robust with clear guidelines to collect short-term count data, to analyze data and develop annual average daily traffic (AADT) adjustment factors, and to estimate AADT volumes. In contrast, bicycle and pedestrian traffic monitoring is an area of work for most transportation agencies. In most agencies, there are a low number of counting sites and limited agency experience to manage a citywide or statewide system of collecting, processing and utilizing non-motorized data. Short duration counts are used to estimate longer duration volumes such as AADT. Because bicycle or pedestrian short-term counts vary dramatically over time and significantly more than motorized vehicle counts, the direct application of motorized vehicle AADT estimation methods may be inadequate. The goal of this paper is to present a methodology that will enhance, if needed, existing AADT estimation methods widely employed for motorized vehicle counts. The proposed methodology is based on the analysis of AADT estimation errors using regression models to estimate a correcting function that accounts for weather and activity factors. The methodology can be applied to any type of traffic with high volume variability, but in this research is applied to a permanent bicycle counting station in Portland, OR. The results indicate that the proposed methodology is simple and useful for finding ideal short-term counting conditions and improving AADT estimation accuracy.