A multidimensional clustering approach to generate regional average truck axle load distribution factors (ALDFs) for North Carolina is presented. The results support the Mechanistic–Empirical Pavement Design Guide (MEPDG). Weigh-in-motion data collected on North Carolina roadways are used in the analysis. A multidimensional clustering analysis based on ALDF data develops representative clusters for different highway functional classifications. Findings show that ALDF clusters have distinct characteristics for primary roads, secondary roads, collectors, and local roads. An easy-to-use decision tree based on available traffic parameters and local knowledge helps the pavement designer select the proper ALDF input. Specific contributions include a multidimensional clustering analysis that is guided by MEPDG damage-based analysis, well-defined ALDF clusters that represent specific traffic patterns in North Carolina, and a decision tree characterized by its simplicity to help pavement designers select ALDF inputs.
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References
1.
ARA, Inc., ERES Consultants Division. Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures. Final report, NCHRP Project 1–37A. Transportation Research Board of the National Academies, Washington, D.C., 2004. http://www.trb.org/mepdg/guide.htm.
2.
Guide for Design of Pavement Structures.AASHTO, Washington, D.C., 1993.
3.
SwanD. J., TardifR., HajekJ. J., and HeinD. K.Development of Regional Traffic Data for the Mechanistic–Empirical Pavement Design Guide. In Transportation Research Record: Journal of the Transportation Research Board, No. 2049, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 54–62.
4.
Cambridge Systematics, Inc.; Washington State Transportation Center; and Chaparral Systems Corporation. NCHRP Report 538: Traffic Data Collection, Analysis, and Forecasting for Mechanistic Pavement Design.Transportation Research Board of the National Academies, Washington, D.C., 2005.
5.
ProzziJ. A., and HongF.Hierarchical Axle-Load Data for Mechanistic–Empirical Design. Presented at 84th Annual Meeting of the Transportation Research Board, Washington, D.C., 2005.
6.
KimJ. R., Titus-GloverL., DarterM. I., and KumapleyR. K.Axle Load Distribution Characterization for Mechanistic Pavement Design. In Transportation Research Record 1629, TRB, National Research Council, Washington, D.C., 1998, pp. 13–23.
7.
LuQ., ZhangY., and HarveyJ. T.Estimation of Truck Traffic Inputs for Mechanistic–Empirical Pavement Design in California. In Transportation Research Record: Journal of the Transportation Research Board, No. 2095, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 62–72.
8.
LuQ., and HarveyJ. T.Characterization of Truck Traffic in California for Mechanistic–Empirical Design. In Transportation Research Record: Journal of the Transportation Research Board, No. 1945, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 61–72.
9.
WangY., HancherD. E., and MahboubK.Axle Load Distribution for Mechanistic–Empirical Pavement Design. ASCE Journal of Transportation Engineering, Vol. 133, No. 8, 2007, pp. 469–479.
10.
TranN. H., and HallK. D.Development and Influence of Statewide Axle Load Spectra on Flexible Pavement Performance. In Transportation Research Record: Journal of the Transportation Research Board, No. 2037, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 106–114.
11.
PapagiannakisA. T., BracherM., and JacksonN. C.Utilizing Clustering Techniques in Estimating Traffic Data Input for Pavement Design. ASCE Journal of Transportation Engineering, No. 132, 2006, pp. 872–879.
12.
SharmaS. C., and WarnerA.Improved Method of Grouping Provincewide Permanent Traffic Counters. In Transportation Research Record 815, TRB, National Research Council, Washington, D.C., 1981, pp. 12–18.
13.
SayyadyF., StoneJ. R., TaylorK. L., JadounF. M., and KimY. R.Clustering Analysis to Characterize Mechanistic–Empirical Pavement Design Guide Traffic Data in North Carolina. In Transportation Research Record: Journal of the Transportation Research Board, No. 2160, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 118–127.
14.
IshakS., ShinH.-C., SridharB. K., and ZhangZ.Characterization and Development of Truck Axle Load Spectra for Implementation of New Pavement Design Practices in Louisiana. In Transportation Research Record: Journal of the Transportation Research Board, No. 2153, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 121–129.
15.
JadounF. M., SayyadyF., KimY. R., and StoneJ. R.Damage-Based Analysis to Guide the Development of MEPDG Axle Load Distribution Factors and Clustering for North Carolina. Presented at 90th Annual Meeting of the Transportation Research Board, Washington, D.C., 2011.
16.
RamachandranA., TaylorK. L., StoneJ. R., and SajjadiS.NCDOT Quality Control Methods for Weigh in Motion Data. Public Works Management and Policy, No. 16, 2011, pp. 3–19.
17.
AnderbergM. R.Cluster Analysis for Applications.Academic Press, Inc., New York, 1973, pp. 131–145.
18.
ArabieP., HubertL. J., and De SoeteG. (eds.). Clustering and Classification.World Scientific, River Edge, N.J., 1996.
19.
RomesburgH. C.Cluster Analysis for Researchers.Lulu Press, Morrisville, N.C., 2004, pp. 129–135.
20.
MojenaR.Hierarchical Grouping Methods and Stopping Rules: An Evaluation. The Computer Journal, Vol. 20, No. 4, 1977, pp. 359–363.
21.
MilliganG. W., and CooperM. C.An Examination of Procedures for Determining the Number of Clusters in a Data Set. Psychometrika, No. 50, 1985, pp. 159–179.
