United States Department of Transportation
i
A Method to Estimate Annual Average Daily Traffic for Minor Facilities for MAP-21 Reporting and Statewide Safety Analysis
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2018-06-01
[PDF-10.95 MB]
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Abstract:This research project develops a simple and reliable method to estimate AADT on non-state roadway segments. Two separate analysis was conducted - non-state upper functional classification roadway segments with AADT less than 10000 and local roads. As the AADT varied based on location, we categorized region 2 non-state upper functional classification roads into four sub-regions and developed default values for each functional classification. For local roads, we determined default values based on sub-regions and the presence of Google Street View. Local roads without Google Street View had lower ADT than local roads with Google Street View. These default values can be used to quickly predict AADT if no other information is available. The analysis of the models developed in the literature review revealed that roadway and geometric information is more important than land use and socio-demographic information in predicting missing AADT. In this research, a simple point based model was developed to predict AADT based on the roadway, geometric, and signage information. We developed a stratified random sampling procedure to select roadway segments while ensuring all sub-regions and functional classification was adequately represented. The relevant variables were collected using Google Street View. An overall region 2 as well as separate sub-regional models were developed. The prediction accuracy of the models was tested on separate validation data. For the non-state upper functional classification roadway system model, the model errors were found to be reasonable on roadway segments with an AADT less than 5000. The sub-regional models provided lower median errors for the coast and valley-rural sub-regions. For local roads, the overall model had a median error of -32 which indicates that the model slightly under-predicts the ADT. The overall model has the lowest median error of 4 for the valley-rural sub-region. The gains in accuracy by using the sub-region model are not high.
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