Thin hot-mix asphalt (HMA) concrete overlays are preventive maintenance treatments used to address minor distresses, increase ride quality, and extend pavement life. This paper determines the long-term effectiveness of such treatments by using three measures of effectiveness: treatment service life, increase in average pavement condition, and area bounded by the performance curve. For each measure of effectiveness, the pavement performance indicators used are the international roughness index (IRI), rutting, and pavement condition rating (PCR). For each measure of effectiveness and performance indicator, treatment benefits were found to lie within a wide range because of the effect of varying levels of weather severity, traffic, and route type. The service life of the treatment ranges from 3 to 13 years (IRI performance indicator), 3 to 14 years (rutting), and 3 to 24 years (PCR). When the increase in average pavement condition is used as the measure of effectiveness, the results show that such treatments offer 18% to 36% decrease in IRI, 5% to 55% reduction in rutting, and 1% to 10% increase in PCR. For the area enclosed by the performance curve, thin HMA overlay effectiveness ranges from 40 to 360 IRI years (where IRI is in inches per mile), 0.13 to 0.76 RUT years (where RUT is in inches), and 7 to 130 PCR years (where PCR is on a 0 to 100 scale). The wide ranges of thin HMA overlay effectiveness for each combination of measure of effectiveness and performance indicator is suggestive of the sensitivity of the treatment effectiveness to levels of traffic loading and weather severity, and route type. The effectiveness of thin HMA overlay treatments is of interest to pavement professionals and is a vital input in the quest for cost-effective long-term pavement preservation practices.

No abstract

No abstract

Incident prediction models are presented for the Interstate 80/Interstate 94 (Borman Expressway in northwestern Indiana) and Interstate 465 (northeastern Indianapolis, Indiana) freeway sections developed as a function of traffic volume, truck percentage, and weather. Separate models were developed for all incidents and noncrash incidents. Three model types were considered (Poisson regression, negative binomial regression, and nonlinear regression), and the results were compared based on magnitudes and signs of model parameter estimates and t-statistics. Least-squares estimation and maximum-likelihood methods were used to estimate the model parameters. Data from the Indiana Department of Transportation and the Indiana Climatology Database were used to establish the relationships. For a given session and incident category, the results from the Poisson and negative binomial models were found to be consistent. It was observed that, unlike section length, traffic volume is nonlinearly related to incidents, and therefore these two variables have to be considered as separate terms in the modeling process. Truck percentage was found to be a statistically significant factor affecting incident occurrence. It was also found that the weather variable (rain and snow) was negatively correlated to incidents. The freeway incident models developed constitute a useful decision support tool for implementation of new freeway patrol systems or for expansion of existing ones. They are also useful for simulating incident occurrences with a view to identifying elements of cost-effective freeway patrol strategies (patrol deployment policies, fleet size, crew size, and beat routes).