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, 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 numbers of counting sites and limited agency experience to manage a city-wide or state-wide system of collecting, processing, and using nonmotorized data. Short duration counts are used to estimate longer duration volumes such as AADT. Because bicycle or pedestrian shortterm counts vary dramatically over time and significantly more than motorized vehicle counts, the direct application of motorized vehicle AADTestimation 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, Oregon. The results indicate that the proposed methodology is simple and useful for finding ideal short-term counting conditions and improving AADT estimation accuracy. DOI: 10.1061/(ASCE)TE.1943-5436.0000663. © 2014 American Society of Civil Engineers. Author keywords: Annual average daily traffic (AADT) estimation; Sampling error; Short-term counts; Bicycle data. Introduction and Motivation Motor vehicle count programs are well established and robust; however, bicycle traffic monitoring is incipient. There is only a small number of established permanent counting sites and limited agency experience to manage a state-wide system of nonmotorized data. From a planning point of view, a key measure of traffic volumes is annual average daily traffic (AADT). AADT represents average daily volume over an entire year at a specific location or facility. The applications of AADT values are numerous and range from safety analysis to prioritization of investments. There are two primary procedures for calculating motorized AADT from permanent, 365-day, 24-h counting stations, also referred to as automated traffic recorders (ATR): one is a simple sum of all daily volumes for one year divided by the number of counting days in that year; the other is an average of averages (FHWA 2012). The AADT calculation for averages of averages from continuous counts comes from the “AASHTO Guidelines for Traffic Data Programs,” prepared in 1992 (AASHTO 1992). One outcome of the method to calculate the average of averages is estimates for day of week (DOW) factors for each month of the year. That is, 84 factors are estimated: Seven factors for each DOW for each of the 12 months of the year. The procedure for the AASHTO method of determining AADT using continuous counts is as follows: 1. Calculate the average for each DOW for each month to derive each monthly average DOW; 2. Average each monthly average DOW across all months to derive the annual average DOW; and 3. The AADT is the mean of all of the annual average DOW. The formula for the AASHTOmethod for determining AADT is