Annual Average Daily Traffic (AADT) is one of the most important indicators of travel demand with many applications in different areas of transportation engineering. Highway agencies commit significant resources to traffic monitoring programs to obtain AADTs, among others. Although many guidelines exist at the national or the provincial levels about how to best operate a traffic monitoring program, there are still quite a few steps relying on subjective judgements, such as how to apply traffic growth rate and how to assign short-term traffic counts (STTCs) to permanent traffic counts (PTCs) or permanent traffic counter groups (PTCGs). In order to reduce risks of potentially significant AADT estimation errors due to the above subjective judgements, PTC data from the province of Alberta, Canada are used to examine spatial correlation in traffic seasonality as well as traffic growth rate for all the road segments covered by its PTC program. The results show that roads in a functional class group can have several seasonal traffic patterns, and there is no definitive relationship between functional class and traffic seasonality. The finding indicates that the STTCs to PTCGs assignment procedure in the Federal Highway Administration’s (FHWA) method may not be appropriate, as it is based on a road’s functional class only. The results also demonstrate that traffic growth rates are highly clustered in the study area, and the correlation analysis revealed that growth rates applied to short-term counting sites should be taken from the closest PTC on a road from a similar functional class. In this regard, Geographical Information Systems (GIS) analyses provide a clear portrait of how traffic growth distributes over a jurisdiction and thus reduces the judgmental errors associated with the task.
