Alternative Seasonal Factor Estimation Methods and Their Accuracy in Predicting Annual Average Daily Traffic

Abstract

The annual average daily traffic (AADT) is a key transportation performance measure used in guiding the allocation of federal funds to US states. It is estimated from daily traffic volume counts recorded over a year. Given its importance, the Federal Highway Administration (FHWA) requires each state to estimate AADT for various sections of their road network. Each state department of transportation (DOT) therefore has a traffic monitoring program for collecting traffic data. It comprises: a permanent traffic count (PTC) program, and a short period traffic count (SPTC) program. PTC volume data are used to estimate AADT, and seasonal factors (SFs). SFs are used to adjust SPTC into AADT estimates.

Most states develop SFs using the most recent year’s PTC data only. However, a few use multiple years’ PTC data. Tennessee Department of Transportation (TDOT) is in the latter category. TDOT’s method implicitly assumes equal reliability of SFs over a five-year period. The objectives of this research were: First, to develop a new procedure for combining SFs from multi-year data by relaxing the assumption underlying TDOT’s method; second, to investigate which SF development method yields more accurate AADT estimates. Data from TDOT and Maryland Department of Transportation were analyzed.

The new procedure developed in this research for combining multi-year SFs predicts AADT most accurately. It is followed by TDOT’s method.