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Journal of Emerging Trends in Computing and Information Sciences >> Call for Papers Vol. 8 No. 3, March 2017

Journal of Emerging Trends in Computing and Information Sciences

Estimating Annual Average Daily Traffic Using Daily Adjustment Factor

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Author Jung-Ah Ha, Ju-Sam Oh
ISSN 2079-8407
On Pages 580-587
Volume No. 5
Issue No. 7
Issue Date August 1, 2014
Publishing Date August 1, 2014
Keywords Annual Average Daily Traffic, Monthly factor, weekly factor, daily factor, grouping


Abstract

This study dealt with estimating AADT which serves the important basic data in transportation sector. AADT estimation is fundamental to the analysis of transportation data sets and the management of transportation systems. AADT is estimated using short-term traffic counts at most sites because permanent traffic counts is installed at limited sites. To estimate AADT, an adjustment factor application model was proposed on FHWA's Traffic Monitoring Guide in the United States. This model uses monthly or weekly adjustment factors to estimate AADT. Additionally, grouping with monthly factor, weekly factor and hourly volume pattern was proposed, but these methods don’t reflect characteristics of daily pattern. So this study used daily factor to estimate AADT and compared with advanced research. Daily factor is produced 365 factors on one permanent traffic count. Accuracy of AADT was enhanced using daily factor because it reflects daily characteristics as compared to monthly or weekly factors. But it is most important to assign a site to its similar site, because unsimilar assignment carries the greatest potential for significant estimation error. Assigning a short term traffic count to permanent traffic counts to apply adjustment factor will be investigated as a future study.
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