The Impact of Factoring Traffic Counts for Daily and Monthly Variation in Reducing Sample Counting Error

Linn County Regional Planning Commission, 6th Floor City Hall, Cedar Rapids, Iowa 52401. Transportation agencies often determine what the annual average daily traffic (AADT) count is on streets and highways by counting traffic for short time periods (usually for 24 hours) and then estimating the AADT based on this count and a numerical factor that takes into account dayof-week and/or seasonal variations in traffic volumes found at a small number of permanent automatic traffic recording stations (ATR’s). Considerable research has been devoted to help state departments of transportation (DOTs) and other agencies develop cost-effective programs to develop factoring procedures to ensure reasonably accurate estimates of AADT from short-term counts. The U.S. DOT has also published estimates of sample error as a function of the volume in an unfactored count. However, no recent research has been found that provides answers or guidance as to how much (sampling) error remains in the estimation of AADT from a factored short-term count in urban areas. Such research is necessary to help agencies determine whether changes in counted volume over time represent a significant change in traffic flow or not, for how long a period of time should a count be taken to reach a desired level of confidence in the count, and to help develop a standard for traffic forecasting model performance regarding the minimization of discrepancies between counted and modeled traffic flows. This paper presents an analysis of just how much day of week/month of year factors can reduce the error of prediction of AADT from a short-term traffic count, utilizing data from an ATR station maintained by the Iowa DOT in Cedar Rapids, Iowa. The benefits of factoring are shown to be a one-quarter reduction in error of AADT prediction for a 24-hour count at this station, with minimal added benefit of a (consecutive) multiple-day count. The metropolitan planning agency will utilize these findings in future evaluations of forecasting model performance.