Impact of Pavement Roughness on Vehicle Free-Flow Speed
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.