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Title:

Clustering Analysis to Characterize Mechanistic–Empirical Pavement Design Guide Traffic Data in North Carolina

Accession Number:

01152424

Record Type:

Component

Availability:

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Order URL: http://trb.org/Main/Blurbs/Data_System...d_Travel_Survey_Methods_2010_164095.aspx

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Order URL: http://worldcat.org/isbn/9780309142885

Abstract:

This paper presents attempts to generate regional average truck axle load distribution factors (ALFs), monthly adjustment factors (MAFs), hourly distribution factors (HDFs), and vehicle class distributions (VCDs) for North Carolina. The results support Mechanistic–Empirical Pavement Design Guide (MEPDG) procedures. Weigh-in-motion data support the analysis and generate seasonal factors. MEPDG damage-based sensitivity analysis shows that pavement performance is sensitive to North Carolina site-specific ALFs, MAFs, and VCDs. Similar results occur for national default values of ALF, MAF, and VCD. Hierarchical clustering analysis based on North Carolina ALFs and MAFs develops representative seasonal traffic patterns for different regions of the state. Findings show that seasonal truck traffic has distinct characteristics for the eastern coastal plain, the central Piedmont, and the western mountains. A simplified decision tree and a related table help the pavement designer select the proper representative patterns of ALF and MAF. To develop VCD factors, the approach uses 48-h classification counts and a seasonal factoring procedure to account for day-of-week and seasonal variations. The approach incorporates site-specific truck traffic to improve the accuracy of pavement design. On the basis of sensitivity analysis results, pavement performance is found to be insensitive to North Carolina site-specific and national default values of HDF; thus, the average statewide HDF values may be used as input to MEPDG. Specific contributions of this research are the relative insensitivity of pavement performance to HDF, the use of 48-h classification counts to estimate VCD inputs, and a decision tree and table to help pavement designers select the proper ALF and MAF inputs.

Monograph Accession #:

01173399

Report/Paper Numbers:

10-1766

Language:

English

Authors:

Sayyady, Fatemeh
Stone, John R
Taylor, Kent L
Jadoun, Fadi M
Kim, Y Richard

Pagination:

pp 118-127

Publication Date:

2010

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 2160
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

9780309142885

Media Type:

Print

Features:

Figures (7) ; References (20) ; Tables (2)

Geographic Terms:

Subject Areas:

Data and Information Technology; Design; Highways; Pavements; I22: Design of Pavements, Railways and Guideways

Files:

TRIS, TRB, ATRI

Created Date:

Jan 25 2010 10:48AM

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