Modern smartphones are equipped with many useful sensors, such as gyroscopes, magnetometers, global positioning system (GPS) receivers, and three-dimensional (3D) accelerometers. In this study, smartphones’ 3D accelerometers were used for collecting a vehicle’s vertical acceleration data. Through the use of various signal processing and pattern recognition techniques, such as cross correlations, Welch periodograms, and variance analyses, the measured signals (time series acceleration data) were identified and correlated with the actual international roughness index (IRI) values. It was found that the variance among the vertical acceleration measurements was the key feature for classifying the measured signals. A validation analysis was also conducted to measure the reliability of this methodology. The initial validation results suggested that, using this methodology, the smartphone used could predict with reasonable certainty the actual IRI values. The study was performed on 35 roadway segments extracted from the Wyoming local roads pavement management system (PMS). Also, the selected segments have various lengths and geometric features reflecting the actual roadway segments under any PMS. The major advantage of this technique includes the low-cost solution of measuring local roadway roughness.

In the state of Colorado, the Colorado Department of Transportation (CDOT) utilizes their pavement management system (PMS) to manage approximately 9,100 miles of interstate, highways, and low-volume roads. Three types of deterioration models are currently being used in the existing PMS: site-specific, family, and expert opinion curves. These curves are developed using deterministic techniques. In the deterministic technique, the uncertainties of pavement deterioration related to traffic and weather are not considered. Probabilistic models that take into account the uncertainties result in more accurate curves. In this study, probabilistic models using the discrete-time Markov process were developed for five distress indices: transverse, longitudinal, fatigue, rut, and ride indices, as a case study on low-volume roads. Regression techniques were used to develop the deterioration paths using the predicted distribution of indices estimated from the Markov process. Results indicated that longitudinal, fatigue, and rut indices had very slow deterioration over time, whereas transverse and ride indices showed faster deterioration. The developed deterioration models had the coefficient of determination (R²) above 0.84. As probabilistic models provide more accurate results, it is recommended that these models be used as the family curves in the CDOT PMS for low-volume roads.

Motor vehicle crashes (MVCs) have a huge cost to society in terms of death, injury and property damage. The cost of fatal MVCs alone is estimated at US $44 billion per year. Among many confounding factors, traffic citations as an element that may reduce MVC frequency are not well understood, and most research carried out to date has evaluated the effects of the total number of citations on the number of MVCs. However, certain types of citations may be more likely to reduce the number of MVCs, whereas other types are not very effective. This research was set out to examine the impact of different types of traffic citations on MVCs on two hazardous main US highways in Wyoming US-30 and US-26. A negative binomial modeling technique was implemented by exploiting 4 years of crash and citations data to identify the causal impacts of traffic citations on crash frequency by incorporating traffic and geometric features. The modeling results showed that higher numbers of speeding and seat belt citations reduce the number of crashes significantly. These findings are the results of law enforcement efforts along the highways. Traffic count and the number of horizontal curves were found to significantly increase the number of MVCs.

A pavement management system (PMS) is a strategic and systematic process to maintain and upgrade the road network. When funding is limited, it is very important to identify the best mix of road preservation projects that provides the maximum benefits to society in terms of overall life cycle cost of the road network. The most common factors that play an important role for identifying projects are the following: budget, traffic volume, Present Serviceability Index (PSI) and risk associated with selecting treatment types. This research develops an optimisation methodology for county paved roads that identify the best mix of preservation projects within budget, maximising traffic (passengers and trucks traffic) on treated roads, maximising the weighted average PSI, and minimising the risk. This methodology will facilitate a statewide implementation of PMS for counties in the state of Wyoming.

This paper developed a pavement serviceability prediction model for county paved roads. Most county paved roads were built decades ago without following minimum design standards. The recent increase in industrial/mineral activities in the State of Wyoming required developing a pavement management system (PMS) for local paved roads. The developed PMS used the pavement serviceability index (PSI) as a pavement performance parameter. The proposed PSI model for local roads is based on: international roughness index, pavement condition index (PCI) and rut depth for flexible pavements only. Ten panellists from Wyoming rated 30 pavement sections that were randomly selected at different distresses’ levels; using two vehicles (SUV and Sedan). The statistical analysis indicated that the seating position, age and gender are not significant to the rating process. However, the vehicle’s type found to be significant. The newly developed model from this study explains 80% of the variations in the PSI values of county roads (adjusted R² = 0.80). In addition, the new model seems to provide more realistic representation of the conditions of county roads than the statewide model used on the state’s highway system.