No abstract

In earlier studies of the environmental impact of pavement roughness on life cycle greenhouse gas (GHG) emissions, it was assumed that pavement roughness (usually measured by International Roughness Index, IRI) has no impact on vehicle speed. However, because ride comfort increases when a pavement becomes smoother (that is, when roughness decreases), it is possible that people will drive faster on a smoother pavement. Because most vehicles achieve maximum fuel efficiency between 40 and 50 mph (64 and 80 km/h), fuel use increases at speeds beyond this range, and this increase in speed might offset the benefits gained from the reduced rolling resistance associated with reduced pavement roughness. Therefore, to investigate the impact of changes in pavement roughness on driving behavior with respect to speed, this study built a linear regression model to estimate free-flow speed on freeways in California. The explanatory variables included lane number, total number of lanes, day of the week, region (Caltrans district), gasoline price, and pavement roughness as measured by IRI. Data from the California freeway network from 2000 to 2011 were used to build the model. The results show that pavement roughness has a very small impact on free-flow speed within the range of this study. For the IRI coverage in this study (90 percent of the records have an IRI of 3 m/km or lower and 90 percent of the records have an IRI change of 2 m/km or lower), a change in IRI of 1 m/km (63 in./mi) resulted in a change of average free-flow speed of about 0.48 to 0.64 km/h (0.3 to 0.4 mph), a value low enough to cause almost no change in fuel use. This result indicates that making a rough pavement segment smoother through application of a maintenance or rehabilitation treatment will not result in substantially faster vehicle operating speeds, and therefore the benefits from reduced energy use and emissions due to reduced rolling resistance will not be offset by the increased fuel consumption that accompany increases in vehicle speed. However, efforts to develop a good model for predicting free-flow speed were not fully successful. The Southern California Interstate Freeway model developed yielded the best result with an adjusted Rsquared of 0.72. For the rest of the regions in the state, the selected explanatory variables can only explain about half of the total variance, meaning that there are still other variables, such as vehicle type, with a substantial impact on free-flow speed that were not covered in this study.

A computer program known as CalME has been developed for analysis and design of new flexible pavements and rehabilitation of existing pavements. The paper describes the overlay design procedure and the calibration of the models for reflection cracking and permanent deformation through heavy vehicle simulator (HVS) tests. To simplify the input process, the program includes databases for traffic loading, climatic conditions, and standard materials. A companion program was developed for backcalculation of layer moduli, and the results may be automatically imported into the CalME database. The program incorporates the existing, empirical California Department of Transportation design methods as well as an incremental–recursive analysis procedure based on the mechanistic–empirical method. The effects of different pavement preservation and rehabilitation strategies on pavement damage may be studied with several options for triggering timing of placement. The influence of within-project variability on the propagation of damage can be evaluated using Monte Carlo simulation. The program also permits importation of the results of HVS or track tests into the database and simulation of the experiments on the computer. This feature is useful for the calibration of the mechanistic–empirical models but may also be used for in-depth interpretation of accelerated pavement testing results. An HVS experiment that was used for calibration of the reflection cracking and the permanent deformation models is described.

No abstract

Unpaved roads have a dynamic surface, which can make it difficult to predict the skid resistance of a section for use in geometric design and gravel selection and to schedule maintenance. This investigation showed that there are three mechanisms for skidding on unpaved roads: intersurface friction, sliding on a thin layer of loose material, and plowing through a thick layer of loose material. The main surface and material properties affecting skid resistance are the stoniness severity and extent, the severity and extent of raveling, and the amount of loose material in the 0.850-mm to 2.00-mm range on the surface. The range of coefficients of friction for unpaved roads is from 0.40 to 0.85, with the lower value being conservative.