Improving the estimation of total and direction-based heavy-duty vehicle annual average daily traffic
Ioannis Tsapakis,
William H. Schneider and
Andrew P. Nichols
Transportation Planning and Technology, 2011, vol. 34, issue 2, 155-166
Abstract:
The estimation of annual average daily traffic (AADT) is an important parameter collected and maintained by all US departments of transportation. There have been many past research studies that have focused on ways to improve the estimation of AADT. This paper builds upon previous research and compares eight methods, both traditional and cluster-based methodologies, for aggregating monthly adjustment factors for heavy-duty vehicles (US Department of Transportation Federal Highway Administration (FHWA) vehicle classes 4--13). In addition to the direct comparison between the methodologies, the results from the analysis of variance show at the 95% confidence level that the four cluster-based methods produce statistically lower variance and coefficient of variation over the more traditional approaches. In addition to these findings -- which are consistent with previous total volume studies -- further analysis is performed to compare total heavy-duty monthly adjustment factors, both directions of traffic, with direction-based monthly adjustment factors. The final results show that the variance as well as the coefficient of variation improve on average by 25% when directional aggregate monthly adjustment factors are used instead of total direction.
Date: 2011
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03081060.2011.554709 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:34:y:2011:i:2:p:155-166
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20
DOI: 10.1080/03081060.2011.554709
Access Statistics for this article
Transportation Planning and Technology is currently edited by Dr. David Gillingwater
More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().