This paper investigates the severity of pothole problems, conducts a critical assessment of current maintenance practices, and identifies resources available for pothole repair based on the results from a questionnaire survey of six provincial transportation agencies in Canada. The survey outcomes indicated a greater percentage of moderate to highly severe potholes in the study area. Freeze-thaw cycles were identified as the most influential factor in pothole formation. A large portion of pothole repair operations are conducted in the summer period. The frequently used patching materials were conventional cold mix, hot-mixed asphalt, Quality Pavement Repair, and Innovative Asphalt Repair. The throw-and-go method is commonly used for pothole repair operations in all seasons. The durability of a winter-repaired patch is significantly less than that of summer. Raveling, edge disintegration, and cracking are the most concerning distresses of patch failure, which could be a result of inadequate stability, adhesion, cohesion, and stripping potential of patching materials.

Preventing weather-related crashes is a significant part of maintaining the safety and mobility of the travelling public during winter months. To help mitigate detrimental effects of winter road conditions, transportation authorities rely on real-time and near-future road weather and surface condition information disseminated by road weather information systems (RWIS) to make more timely and accurate winter road maintenance-related decisions. However, the significant costs of these systems motivate governments to develop a framework for determining a region-specific optimal RWIS density. Building on our previous study to facilitate regional network optimization, this study is aimed at considering the nature of spatiotemporally varying RWIS measurements and integrating larger case studies comprising eight different US states. Space-time semivariogram models were developed to quantify the representativeness of RWIS measurements and examine their effects on regional topography and weather severity for improved generalization. The optimal RWIS density for different topographic and weather severity regions was then determined via one of the most successful combinatorial optimization techniques—particle swarm optimization. The findings of this study revealed a strong dependency of optimal RWIS density on varying environmental characteristics of the region under investigation. It is anticipated that the RWIS density guidelines developed in this study will provide decision makers with a tool they need to help design a long-term RWIS implementation plan.

This study represents an advanced approach to road weather information system (RWIS) network planning. Here, a methodological framework is developed to determine optimal RWIS locations by integrating two analysis domains: space and time. Using a case study, the application of the proposed method is demonstrated using three critical RWIS variables: air temperature, road surface temperature, and dew point temperature. With these three variables, a series of geostatistical semivariogram analyses are performed to construct a single spatiotemporal model named joint semivariogram, which is able to preserve both spatial and temporal aspects. The constructed joint semivariogram is then used to find the optimal RWIS locations for a randomly generated study area using a popular heuristic algorithm—spatial simulated annealing. The proposed method enhances the previously developed RWIS location allocation model by considering both spatial and temporal components of multiple variables. The finding from this analysis reveals that optimal RWIS location strongly depends on the spatiotemporal autocorrelation structure of the variable of interest. Consequently, location solutions generated using the three variables are found to be different from each other. The variation among the RWIS location solutions is then further quantified by developing a spatial similarity index that is used to measure the degree of spatial similarities between different variables. Overall, the findings documented in this study will provide RWIS planners with a more complete and conclusive location allocation strategy and can act as a decision support tool for long-term RWIS network planning.

This paper extends the previously developed method of optimizing Road Weather Information Systems (RWIS) station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables. Previous research primarily centered on single-variable analysis focusing on road surface temperature (RST). The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework. This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty. To demonstrate the effectiveness of this refinement, the authors applied the framework to Maine’s existing RWIS network, conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm. The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations. A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations. The location solutions generated have been adopted by MaineDOT for future implementations, attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.

Optimal RWIS network can be defined as an RWIS configuration where the total number of stations (RWIS density) are determined based on a well-established guideline and the locations are allocated systematically assuming that it will provide the maximum monitoring coverage of the network. This paper examines and quantifies the benefit of an optimized RWIS network and how these benefits impact traffic safety. The methodological framework presented herein builds upon our previous efforts in RWIS location-allocation, where the kriging variance is used as a performance indicator for monitoring coverage. In this study, the network coverage index (NCI) parameter is proposed to gauge RWIS network performance and quantitatively evaluate its impact on traffic safety. The findings of this study reveal a strong dependency between the NCI and the RWIS network configuration. In terms of traffic safety, the relationship between NCI and safety effectiveness can be expressed as a polynomial function, where the two are proportional to one another. In the state of Iowa, an RWIS network with 80% monitoring coverage (NCI = 0.8) can reduce additional 40 collisions per site annually compared to a network without RWIS stations. Based on the findings obtained in this study, road agencies and RWIS planners can now be assisted with conceptualizing the capabilities of an optimized RWIS network, which will help them increase monitoring coverage, and in the process, gain a quantitative understanding on its potential impact on traffic safety.