Traffic Data Collection Requirements for Reliability in Pavement Design

Abstract

This paper presents a comprehensive approach for establishing the minimum traffic data collection requirements for predicting pavement life within an acceptable error, given a reliability level. Pavement life is predicted for 30 long term pavement performance (LTPP) sites using the NCHRP 1-37A Pavement design guide (PDG). Seventeen distinct traffic data collection scenarios are simulated using extended coverage (i.e., more than 299 days/year) weigh-in-motion (WIM) data obtained from the LTPP database. These scenarios involve combinations of site-specific, regional, and national data, including total truck counts, truck counts by class, and axle load distributions by axle type. Regional data are obtained from LTPP sites in the same jurisdiction as the 30 sites analyzed, using clustering techniques. The PDG software defaults are assumed to be representative of national data. For each scenario involving discontinuous time coverage, the lower range in each of the input elements to the PDG is calculated for confidence levels of 75, 85, and 95%. For each scenario and confidence level, pavement life errors are predicted with respect to the life estimated by the complete extended coverage WIM dataset. Two sources of error are identified: one from specifying the mean value for all traffic input and the other from specifying the lowest percentile for all traffic input simultaneously (i.e., the latter is applicable to discontinuous coverage scenarios only). Overall error is computed by adding 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 reliability level, versus traffic data collection scenario. Selecting the first two as a function of the importance of a roadway facility allows identifying the minimum traffic data collection effort required.