Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. Due to cost limitations, AADT data is not typically collected for local roads, however, the necessity of having AADT data for the purpose of making safety decisions is not diminished in the case of local roads, so a methodology was required to be able to generate AADT data in areas where manually acquiring data is not economically feasible. This research was conducted to develop models that can accurately estimate AADTs within a small or medium sized community. The models use a combination of roadway and socio-economic factors within a quarter-mile buffer of the desired count location. The models were tested using a collection of statistical tests to ensure the robustness of the models, validated to additional data collected for the community, and a transferability test of the models was performed to test the ability of the model to accurately predict across different communities of similar size. The results of the paper indicate that direct demand AADT estimation models can be accurately developed and transferred to other communities of similar size to support AADT estimation on desired roadways in different communities.
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