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Transportation Planning and Technology Volume 34, 2011 - Issue 2
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Articles

Improving the estimation of total and direction-based heavy-duty vehicle annual average daily traffic

Ioannis Tsapakis Department of Civil Engineering, The University of Akron, Auburn Science and Engineering Center, Akron, OH, 44325-3905, USA
,
William H. Schneider IV Department of Civil Engineering, The University of Akron, Auburn Science and Engineering Center, Akron, OH, 44325-3905, USACorrespondence[email protected]
&
Andrew P. Nichols Weisberg Division of Engineering and Computer Science, Marshall University, Huntington, WV, 25755, USA
Pages 155-166 | Received 23 Oct 2009, Published online: 03 Mar 2011

Citations (4)

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Read on this site (1)

Ioannis Tsapakis & William H. Schneider IV. (2015) Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics. Transportation Planning and Technology 38:2, pages 181-199.
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Articles from other publishers (3)

Md Mehedi Hasan & Jun-Seok Oh. (2020) GIS-Based Multivariate Spatial Clustering for Traffic Pattern Recognition using Continuous Counting Data. Transportation Research Record: Journal of the Transportation Research Board 2674:10, pages 583-598.
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Ioannis TsapakisWilliam H. Schneider. (2019) Use of Support Vector Machines to Assign Short-Term Counts to Seasonal Adjustment Factor Groups. Transportation Research Record: Journal of the Transportation Research Board 2527:1, pages 8-17.
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Ioannis TsapakisWilliam H. SchneiderIVIVAndrew P. NicholsJames Haworth. (2014) Alternatives in assigning short-term counts to seasonal adjustment factor groupings. Journal of Advanced Transportation 48:5, pages 417-430.
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