This study deals with the sensitivity of the NCHRP 1-37A Pavement Design Guide predictions to traffic data input. A number of traffic data collection scenarios are simulated with the use of extended coverage (more than 299 days per year) weigh-in-motion (WIM) data from the Long-Term Pavement Performance (LTPP) database. These scenarios consist of combinations of site-specific, regional, and national data, including total truck counts, truck counts by class, and axle-load distributions by axle type. For each simulated scenario, traffic input to the NCHRP 1-37 Pavement Design Guide is estimated with the methodologies prescribed in the Traffic Monitoring Guide. For discontinuous time coverage scenarios, all possible time coverage combinations are considered, and the range in the estimated traffic input parameters is computed. Pavement life predictions are obtained under mean traffic input for all traffic data collection scenarios and under low percentile input for the discontinuous traffic data collection scenarios. For each scenario and confidence level, pavement life errors are predicted for the life estimated by the complete extended coverage WIM data set. Two sources of error are identified: one from specifying the mean value for all traffic input and one from specifying the lowest percentile for all traffic input simultaneously (the latter applies to discontinuous coverage scenarios only). Overall error is computed by the addition of the range in the mean of the second error component to the range of the first error component. The results are in the form of plots of pavement life prediction error, versus confidence level, versus traffic data collection scenario.


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    Titel :

    Sensitivity of NCHRP 1-37A Pavement Design to Traffic Input


    Weitere Titelangaben:

    Transportation Research Record


    Beteiligte:
    Papagiannakis, A. T. (Autor:in) / Bracher, M. (Autor:in) / Li, J. (Autor:in) / Jackson, N. (Autor:in)


    Erscheinungsdatum :

    2006-01-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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