Khaled Ksaibati

Preventive treatment selection should be based on the right climatic and environmental conditions to achieve maximum pavement performance. This is possible if treatment performance and the factors that influence it are investigated. Unlike previous studies that emphasized pavement characteristics that influenced the effectiveness of treatments, the linear mixed model used in this study includes fixed effects that consider treatment types, climates, and pavement characteristics; and random intercepts for section-level performance (nested within states). This accounts for repeated observations on the same pavement sections as treatments aged, while adjusting for unobserved section- and state-level differences. The aim was to evaluate the long-term effectiveness of asphalt thin overlay, slurry seal, chip seal, and crack sealing on pavements across the four climatic regions of US and Canada using longitudinal distress data from the Long-Term Pavement Performance program. From the results, the random effects explained a significant portion of the variability in pavement distress. Pavement roughness and rutting differences were greater at the section level than across states. States like Nebraska and Montana had higher rutting values than the national average. There was also an increasing post-treatment trend of pavement deterioration in all four climates. Asphalt thin overlay outperformed all other treatments and the control in the wet-freeze regions. Chip seal remains a viable alternative for treating pavement rutting, aside from asphalt thin overlay. Pavements with higher pretreatment roughness significantly led to higher posttreatment roughness and rutting. These insights provide transport agencies with a better understanding of the performance of treatments in different climates.

Road intersection crashes remain a major contributor to injuries due to complex conflict patterns and multimodal interactions. Among the factors influencing intersection safety, inadequate intersection sight distance (ISD) attributed to roadside sight obstructions can limit drivers’ ability to respond to conflicting movements, potentially affecting crash injury outcomes. Despite its importance, visual obstruction has rarely been examined as a distinct context in traffic crash injury severity modeling. This study investigates crash injury severity at visually obstructed intersections using an ordinal probit modeling framework applied to 951 intersection crashes documented with sight obstruction as a contributing factor in Wyoming over the period 2014 through 2023. Crash data were analyzed to identify the effects of driver behavior, vehicle characteristics, roadway geometry, environmental conditions, and traffic control on ordered injury severity outcomes ranging from property damage only (PDO) to fatal and serious injury. Nonparametric bootstrap resampling with 1000 iterations was employed to assess parameter stability and construct empirical confidence intervals. Average marginal effects were estimated to quantify the change in probability of each injury severity level associated with key predictors. The results indicate that alcohol involvement produces the largest severity shift, reducing the probability of PDO outcomes by 51.2 percentage points while increasing the probability of fatal and serious injury by 34.2 percentage points. Hillcrest grade locations increase fatal and serious injury risk by 14.4 percentage points, while adverse road surface conditions, including snowy, icy, and wet pavements, consistently reduce fatal and serious injury probability by 12.5 to 15.1 percentage points, reflecting behavioral adaptation to visually salient hazard cues. Bootstrap validation confirms strong parameter stability across all estimates, with 94% of parameters showing bootstrap standard errors within 25% of their asymptotic counterparts. By formally establishing visually obstructed intersections as a dedicated severity modeling context and integrating systematic bootstrap validation, this study contributes both substantive and methodological insights to support evidence-based prioritization of intersection safety improvements.

Adequate pavement marking retroreflectivity R _L is crucial in enhancing road safety, and cost-effective marking restriping planning requires R _L deterioration models and service life estimation. This paper presents the Bayesian ordered logit model that incorporates Markov Chain Monte Carlo simulation to predict the degradation trend in R _L using data collected from representative highways in Wyoming. The results indicate that the marking age and characteristics, geometric locations, pavement surface, traffic volume, and snowplow operations are the significant factors affecting pavement marking degradation. The results indicate that the lane line and center line are more prone to degradation compared with the edge line. In addition, the degradation rate is higher for white markings than for yellow markings and in the curve section compared with the straight section. Cumulative traffic, truck traffic passage, and snowplow operation also significantly affect R _L degradation. The outcomes derived from the distinct models reveal substantial differences among the three functional classification models and justify investigating separate models. By integrating the random intercept into the modeling process, the analysis found a notable presence (from 22% to 31%) of unobserved heterogeneity within the same route, a crucial aspect that ensures the attainment of accurate and unbiased results. This paper also proposed a prediction equation to determine pavement marking service lives and reveals that the service life of different materials varies based on road classification, marking material, and line position. The proposed model is readily implementable and can assist pavement marking management by effectively scheduling restriping and maintenance periods for different materials.

Intersections represent critical crash locations on road networks necessitating targeted safety interventions. This study employs a random parameters ordered logit (RPOL) model with heterogeneity in means to analyze injury severity contributing factors across 9108 Wyoming intersection crashes that occurred from 2007 to 2017. The analysis reveals that crashes on principal and minor arterial intersections are consistently associated with higher risks of severe/fatal injuries, while urban intersections exhibit less severe consequences, likely due to lower speeds and enhanced infrastructure. Adverse weather conditions, particularly snowy and icy road surfaces, increase the likelihood of property-damage-only (PDO) outcomes while reducing severe/fatal injuries. Temporal trends show a decline in crash severity over time, coinciding with advances in vehicle safety and policy improvements. Key behavioral factors, including left turn maneuvers and driver’s age heterogeneity, influence crash outcomes, whereas intersection sight distance (ISD) had no significant effect on crash severity underscoring data limitations requiring advanced analysis methods. This study’s findings prioritize the reconsideration of arterial intersection design, urban safety enhancements, and behavior-focused countermeasures for intersection safety.

Effective pavement management systems are essential for accurately predicting pavement conditions and efficiently planning and scheduling maintenance, rehabilitation, and reconstruction activities. Significant efforts are dedicated to developing accurate pavement condition prediction models using machine learning (ML) at the state level. Conversely, insufficient investment, poor quality, and large variations in local roads data have resulted in less attention to modeling local pavement conditions. This study develops eight Bayesian-optimized single-estimator and ensemble ML classification models to predict local pavement roughness. Moreover, the classification granularity of pavement condition was investigated to assess its impact on the predictive power of various ML models. The results reveal that ML classification models with fewer classes exhibit higher accuracy and more stability in precision over recall values, in contrast to models with larger number of classes. The ensemble ML models surpass their single-estimator counterparts, with the category boosting algorithm demonstrating the highest performance, achieving testing accuracies of 0.77 and 0.65 for the three-level and five-level classifications, respectively. Hence, it is recommended to employ ensemble ML algorithms and a smaller number of classes to develop reliable, accurate, and stable predictive models for local roads with imbalanced condition data. This research helps transportation agencies improve their pavement condition prediction, thereby optimizing pavement management and resource allocation.

Despite advancements at the state level, many local highway agencies administering county roads lack an established pavement management system. This is often due to insufficient resources, technical expertise, and challenges related to data availability and quality. This research paper introduces a hybrid system for prioritizing pavement maintenance and rehabilitation (M&R) of Wyoming county roads, integrating pavement condition data, expert perspectives, and road characteristics. The Analytic Hierarchy Process (AHP) was employed to compute the relative significance of pavement conditions and various road characteristics. The results reveal that traffic volume is the most influential factor, accounting for 27% of the overall importance, underscoring its critical role in M&R prioritization. Speed limit and land use also play significant roles, each contributing approximately 20% to the decision-making process. The findings reveal a preference for prioritizing pavement condition over road characteristics, with a correlation coefficient of 0.85 between rankings based solely on pavement condition and those derived from the hybrid system. A 20% discrepancy was observed between the top third pavement segments identified by the two approaches, with even greater differences noted in the second-top-third segments. This research provides a robust framework that not only assists Wyoming counties in effective and efficient road management but also offers valuable guidelines for other states to enhance their pavement M&R strategies.

The degradation of paved and unpaved roads stands as a critical concern in contemporary infrastructure management. When faced with limited budgets, it is important to identify the optimal combination of road preservation strategies to minimize the lifecycle cost of the road network. Specific studies are necessary to improve the maintenance management systems and analyze the behavior of road surface deformation. To narrow these knowledge gaps, this study investigates a management system that focuses on the application of optimization techniques for managing both paved and unpaved tropical roads. Probabilistic deterioration models were constructed using the Markovian process, resulting in precise degradation curves in the context of 18 unpaved road segments in the Zona da Mata County of Minas Gerais (Brazil), along with 88 paved roads located in Minas Gerais. An optimization algorithm was proposed for the prediction of maintenance resources for unpaved and paved roads, emphasizing the cost-effectiveness of preventive and minor rehabilitation treatments over reconstruction. Comparisons between the maintenance costs of unpaved and paved roads indicated that the full rehabilitation costs of paved roads were approximately 10 times higher per kilometer compared to those of unpaved roads. The models effectively captured the trend wherein a major treatment leads to minor additional treatments being necessary for the subsequent several years in both scenarios. The findings of this study provide future directions for the optimized allocation of resources in the management of transportation infrastructures.