Evaluating Annual Average Daily Traffic Calculation Methods with Continuous Truck Traffic Data

Abstract

Traffic volume, often measured in relation to annual average daily traffic (AADT), is a fundamental output of traffic monitoring programs. At continuous count sites, unusual events or counter malfunctions periodically cause data loss, which influences AADT accuracy and precision. This paper evaluates five methods used to calculate AADT values from continuous count data, including the use of a simple average, the commonly adopted method developed by AASHTO (the AASHTO method), and methods that incorporate adjustments to the AASHTO method. The evaluation imposes data removal scenarios designed to simulate real-life causes of data loss to quantify the accuracy and precision improvements provided by these adjustments. Truck traffic data are used to reveal issues arising when volumes are low or when they exhibit unusual temporal patterns. Unlike the AASHTO method, which incorporates a weighted average and an hourly base time period, the FHWA method provides the most accurate and precise results in all data removal scenarios, according to the evaluation. Specifically, when up to 15 days of data are randomly removed, application of the FHWA method can be expected to produce errors within approximately é1.4% of the true AADT value, 95% of the time. Results also demonstrate that including a weighted average improves AADT accuracy primarily, whereas the use of hourly rather than daily count data influences precision. If possible, practitioners contemplating the adoption of the FHWA method should assess its relative advantages within their local context.