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The purpose of this paper is to provide a robust process to statistically analyze reflective cracking field performance data. There is often a lack of consistency and transparency in statistical analysis of pavement field performance data, which may not satisfy ANOVA or regression modeling assumptions. Twelve full-scale asphalt concrete (AC) overlay pavement test sections located at the MnROAD test facility are used to demonstrate the statistical framework. The percentage of cracking reported at joint locations (%RC) is used to represent reflective cracking performance, and its relationships to pre-overlay load transfer efficiency (LTE), truck traffic, overlay thickness, and common performance indices determined from laboratory tests are investigated. The three laboratory tests considered in this study are the disk-shaped compact tension (DCT), semi-circular bend (SCB) and overlay tester (OT). Logistic regression models are used for estimation. Predictive abilities of various models are compared in relation to the percentage odds (%odds) of reflective cracking. Model output is binary: it estimates not the relative amount of reflective cracking but instead the probability of a given pavement structure cracking. Varying ability to perform asphalt mixture laboratory performance testing is assumed. One such model, where no laboratory performance testing variables are included, shows that a one-unit increase (1-in.) in AC overlay thickness may result in approximately a third decrease in the %odds of reflective cracking. A logistic regression model developed that considers laboratory performance data from DCT, SCB, and OT results in the optimal model balancing best fit and best prediction properties without overfitting.

A method to assess the impacts of forecasted climate change on pavement deterioration is presented. Traditional methods of pavement design use historic climate data and assume that climate is stationary with time. Climate change challenges this assumption of stationarity (i.e., natural driving forces of engineering have a variability described by a time-invariant probability density function). Therefore, the use of historic climate data is insufficient for the prediction of climate conditions. The focus is on the preparation and the use of climate model data sets as inputs to the Mechanistic-Empirical Pavement Design Guide (MEPDG) model to simulate flexible pavement performance and deterioration over time. The method is illustrated with a case study that uses future climate model temperature data from three North American Regional Climate Change Assessment Program scenarios at four sites across New England. Pavement distress predicted with future temperature scenarios is compared with that from MEPDG temperature data. Application of the method demonstrates the importance of matching the overlapping periods before using climate forecast output in the MEPDG. Although the simulated impact of future temperature changes on pavement performance was negligible for alligator cracking at the four study sites, asphalt concrete rutting differences were great enough to warrant additional consideration and to suggest that climate change and variability in future climate scenarios could affect pavement design and evaluation. The proposed method can be used to evaluate the impact of other climate variables alone or in combination. The method also can readily use new climate model output and be adapted for new downscaling methods.

The primary purpose of this study is to understand better the roadway performance and the conditions that trigger congestion. Incidents of recurrent congestion were encountered at a location on New Hampshire Interstate 93 northbound, where radar measurements of speed were continuously recorded from April through November 2010. The root cause for the onset of congestion, both recurrent and nonrecurrent, is impossible to discern and explain by using exploratory data analyses alone; the data are too noisy. A time series modeling approach suggests that the magnitude of traffic flow and volatility, measured as the second (variance) and third (skewness) moments of flow residuals, can explain the triggering of congestion events for a highly variable environment that changes time by the time and day. The approach includes two types of mathematical models: generalized additive binomial probability models for roadway congestion that use functions of traffic flow and volatility as explanatory variables and functional data models that decompose and smooth traffic data to add insight to the roles that flow and volatility play in the congestion process. Most notably, the probability of congestion is shown to be a function of the short-term history of flow and volatility. The changes in these values—the first derivatives of flow and second and third moments of flow residuals derived from functional data model—are shown as well. Model selection, parameter estimation, and checking are presented.

The primary purpose of this study is to understand better the roadway performance and the conditions that trigger congestion. Incidents of recurrent congestion were encountered at a location on New Hampshire Interstate 93 northbound, where radar measurements of speed were continuously recorded from April through November 2010. The root cause for the onset of congestion, both recurrent and nonrecurrent, is impossible to discern and explain by using exploratory data analyses alone; the data are too noisy. A time series modeling approach suggests that the magnitude of traffic flow and volatility, measured as the second (variance) and third (skewness) moments of flow residuals, can explain the triggering of congestion events for a highly variable environment that changes time by the time and day. The approach includes two types of mathematical models: generalized additive binomial probability models for roadway congestion that use functions of traffic flow and volatility as explanatory variables and functional data models that decompose and smooth traffic data to add insight to the roles that flow and volatility play in the congestion process. Most notably, the probability of congestion is shown to be a function of the short-term history of flow and volatility. The changes in these values—the first derivatives of flow and second and third moments of flow residuals derived from functional data model—are shown as well. Model selection, parameter estimation, and checking are presented.

A method to assess the impacts of forecasted climate change on pavement deterioration is presented. Traditional methods of pavement design use historic climate data and assume that climate is stationary with time. Climate change challenges this assumption of stationarity (i.e., natural driving forces of engineering have a variability described by a time-invariant probability density function). Therefore, the use of historic climate data is insufficient for the prediction of climate conditions. The focus is on the preparation and the use of climate model data sets as inputs to the Mechanistic–Empirical Pavement Design Guide (MEPDG) model to simulate flexible pavement performance and deterioration over time. The method is illustrated with a case study that uses future climate model temperature data from three North American Regional Climate Change Assessment Program scenarios at four sites across New England. Pavement distress predicted with future temperature scenarios is compared with that from MEPDG temperature data. Application of the method demonstrates the importance of matching the overlapping periods before using climate forecast output in the MEPDG. Although the simulated impact of future temperature changes on pavement performance was negligible for alligator cracking at the four study sites, asphalt concrete rutting differences were great enough to warrant additional consideration and to suggest that climate change and variability in future climate scenarios could affect pavement design and evaluation. The proposed method can be used to evaluate the impact of other climate variables alone or in combination. The method also can readily use new climate model output and be adapted for new downscaling methods.

The possibility of a bimodal log-likelihood function arises with certain data when the combined removal and signs-of-activities estimator is used. Bimodal log-likelihoods may, in turn, yield disjoint confidence intervals for certain confidence levels. The hypothesis that bimodality is caused by the violation of the equal catchability assumption of the removal model, leading to the combination of contradictory data/models in the combined estimator is set forth. Simulations exploring the effect of the violation of removal model assumptions on estimation and inference showed that the assumption of unequal capture probability influenced the frequency of bimodal likelihoods; similarly, extreme parameter values for probability of capture influenced the number of excessively large confidence intervals produced. A sex-specific combined estimator is developed as a remedial model tailored to the problem. The simulations suggest that both the signs-of-activities estimator and the sex-specific estimator perform equally well over the range of simulations presented, though the signs-of-activities estimator is easier to implement.