Abstract
The role of traffic data in the Mechanistic-Empirical (M-E) pavement design is significantly crucial in assessment of pavement performance throughout the pavement design life. Providing a well-qualified traffic data for the M-E pavement design requires a huge effort with respect to collecting and analyzing the data. The Texas Department of Transportation (TxDOT) manages 30 Weighin-Motion (WIM) stations and provided classification and weight data used for this study. Evaluating the classification data first, vehicle classification distribution along with the percentage of trucks, average annual daily truck traffic, and monthly adjustment factor were established. Processing axle load data provided the number of axles per truck and axle load spectra for each of the traditional 13 vehicle classes. To develop statewide axle load spectra data, the cluster analysis was conducted using vehicle classification distribution and the Class 9 tandem axle load spectra data to provide traffic input data for the Texas M-E flexible pavement design program where the load spectra data are not available due to the absence of WIM station. As a result, six clusters for both variables were identified and a guideline was successfully established to use those clusters to generate axle load spectra inputs for a given set of truck traffic classification data.
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References
American Association of State Highway and Transportation Officials (AASHTO) (1993). Guide for design of pavement structures, Washington, D.C.
Applied Research Associates (ARA) (2004). Guide for mechanisticempirical design of new and rehabilitated pavement structures, NCHRP Report I-37A, TRB, National Research Council, Washington, D.C.
Hajek, J. J., Selezneva, O. I., Mladenovic, G., and Jiang, Y. J. (2005). Estimating cumulative traffic loads, Volume II: Traffic data assessment and axle load projection for the sites with acceptable axle weight data, Final Report, FHWA-RD-03-094, Federal Highway Administration, McLean, VA.
Hardle, W. and Simar, L. (2003). Applied multivariate statistical analysis, Springer-Verlag, New York.
Li, S., Nantung, T., and Jiang, Y. (2005). “Assessing issues, technologies, and data needs to meet traffic input requirements by mechanisticempirical pavement design guide.” Transportation Research Record: Journal of the Transportation Research Board, No. 1917, Transportation Research Board of the National Academies, Washington, D.C., pp. 141–148, DOI: 10.3141/1917-16.
Lu, Q. and Harvey, J. T. (2006). “Characterization of truck traffic in california for mechanistic-empirical design.” Transportation Research Record: Journal of the Transportation Research Board, No. 1945, Transportation Research Board of the National Academies, Washington, D.C., pp. 61–72, DOI: 10.3141/1945-08.
Lu, Q. and Harvey, J. T. (2009). “Estimation of truck traffic inputs for mechanistic-empirical pavement design in California.” Transportation Research Record: Journal of the Transportation Research Board, No. 2095, Transportation Research Board of the National Academies, Washington, D.C., pp. 62–72, DOI: 10.3141/2095-07.
Texas Department of Transportation (2003). A strategic plan for weighin-motion compliance, Austin, TX.
Tran, N. H. and Hall, K. D. (2007). “Development and significance of statewide volume adjustment factors in mechanistic-empirical pavement design guide.” Transportation Research Record: Journal of the Transportation Research Board, No. 2037, Transportation Research Board of the National Academies, Washington, D.C., pp. 97–105, DOI: 10.3141/2037-09.
Wang, K. C. P., Li, Q., Hall, K. D., Nguyen, V., and Xiao, D. X. (2011). “Development of truck loading groups for the mechanistic-empirical pavement design guide.” Journal of Transportation Engineering, Vol. 137, No. 12, pp. 855–862, DOI: 10.1061/(ASCE) TE.1943-5436.0000277.
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Oh, J., Walubita, L.F. & Leidy, J. Establishment of statewide axle load spectra data using cluster analysis. KSCE J Civ Eng 19, 2083–2090 (2015). https://doi.org/10.1007/s12205-014-0374-9
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DOI: https://doi.org/10.1007/s12205-014-0374-9