International Journal of Traffic and Transportation Engineering 2016, 5(2): 27-31 DOI: 10.5923/j.ijtte.20160502.01 Developing Direct Demand AADT Forecasting Models for Small and Medium Sized Urban Communities Mehrnaz Doustmohammadi * , Michael Anderson Department of Civil and Environmental Engineering, The University of Alabama in Huntsville, Huntsville, USA Abstract 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. Keywords Annual Average Daily Traffic, Statistical Analysis, Urban Communities 1. Introduction Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses, safety assessments, maintenance schedules, capacity improvements, etc. AADT is defined as the average 24-hour volume at a highway location over a full year. The amount of labor and the associated costs required to collect actual AADT data for every roadway in a community, even for a small area, are so extreme that it is unfeasible that any community would be able to gather AADT data for every roadway in the community to fully support the transportation analyses previously mentioned. Due to cost limitations, AADT data is typically extrapolated from selected 48-hour data counts taken along major roadways in a community, excluding local roads from the data collection efforts. However, the importance of AADT is not diminished on local roads and the relevance of the data to support public safety decisions and traffic flow analysis still exists. For example, one area where having quality AADT data is in the calculation of crash rates on local roads and the implementation of crash reduction factors. In order to accurately identify areas in which high crash levels occur and to successfully appropriate money wisely on potential improvements, * Corresponding author: md0033@uah.edu (Mehrnaz Doustmohammadi) Published online at http://journal.sapub.org/ijtte Copyright © 2016 Scientific & Academic Publishing. All Rights Reserved quality AADT data is required. The demand for quality AADT data on local roads, coupled with the lack of available accurate data, has prompted this research to examine and develop a model 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 model objectives were to be able to take data near a desired AADT site and develop a reasonable estimate of the AADT. In this study, two medium-sized cities (with metropolitan populations roughly 300,000) and two smaller cities (with metropolitan populations roughly 80,000) were selected. Two models were developed, one for each population size city. 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. 2. Literature Review The collection and development of models for the development and estimation of AADT is not a novel concept