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Numerous factoring and baseline values are required to ensure annual average daily traffic (AADT) data are collected and reported correctly. The variability of numerous methods currently used are explored so that those in the traffic community will clearly know the limitations and the extent of each method used and how to properly utilize methods for their agency to obtain the necessary results. Federal Highway Administration (FHWA) Travel Monitoring Analysis System (TMAS) data from 14 years consisting of 24 hours of the day and 7 days of the week volume data from over 6000 continuous permanent volume traffic data sites in the United States comprised the reference dataset for this research. Randomly selected (with some constraints) sites each include one year of 100% complete daily reporting and the set of sites represent 12 functional classes, years 2000 through 2013, 43 states and DC, and various volume ranges. Four AADT estimation methods were examined for accuracy when data from various time periods were removed. This report is a final task report that summarizes identified inaccuracies with current methods that are used for AADT estimation, and includes the analysis methodology and summary statistics findings.
This guide is designed to provide direction on the monitoring of traffic characteristics. It begins with a discussion of the structure of traffic characteristics monitoring and traffic counting. The next two sections cover vehicle classification and truck weighing. The last section presents the coordinated record formats for station identification, traffic volume, vehicle classification, and truck weight data.
This report summarizes the evaluation of Streetlight Data's Annual Average Daily Traffic (AADT) product by comparing with Oregon Department of Transportation's automatic traffic recorder data and AADT estimates derived from short term counts in Bend, MPO. Using an evaluation methodology established by Turner et al. (2008), the report concludes that accuracy was measured to be 18% (N = 172) median (mean equals 25%) absolute percent error for the automatic traffic recorder comparison and 32% (N = 66) median (mean equals 59%) absolute percent error for the short term based AADT estimate comparison. Other measures of data quality were reasonable but the accessibility data quality measure would suffer for network wide (all segments in the network) data accessibility since the setting gates for Streetlight Data's process would be time consuming. Data quality is difficult to fully answer outside the context of a use of the data and its recommended that future evaluations apply the AADT product in a safety, vehicle miles traveled, or air quality analysis to determine the magnitude of difference between currently available data (from models or traditional traffic counts) and these third party data products. Future evaluations should also look at any potential costs savings of purchasing third party data compared with traditional traffic count data collection.