In studies involving asphalt mixtures—such as structural pavement design, service life estimation, suitability analysis, or mix design—it is essential to know the traffic volume that uses the roadway, expressed as AADT (Annual Average Daily Traffic), which represents the average daily volume over a full calendar year.
This value averages out traffic volumes generated by non-regular or intermittent activities (such as study, work, vacations, leisure, etc.). Due to this variability, continuous traffic counts are required to accurately determine the AADT, which often involves large-scale efforts and considerable resources.
In tactical and operational tasks (medium- and short-term), or in immediate-response situations where sufficient resources or data are not available, this becomes particularly challenging. For this reason, sporadic or short-term traffic counts are often used, and due to the lack of historical data, these are typically extrapolated subjectively by professionals who may not have a direct background in traffic engineering. This introduces a high degree of uncertainty, ultimately undermining the validity of other parameters that are otherwise based on accurate and reliable data, resulting in extremely low confidence levels in subsequent applications.
For these reasons, this master’s thesis aims to develop, disseminate, and apply an objective methodology for extrapolating short-term traffic counts into AADT values. The methodology is intended for application across a broad homogeneous region in Argentina, specifically the provinces of Buenos Aires, Santa Fe, Córdoba, Entre Ríos, and La Pampa.