Abstract
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.
Highlights
Intelligent Transportation Systems (ITS) are an important part of modern transportation engineering and have a significant impact on society and our everyday life
If more than one location has the same probability for an road weather information system (RWIS) station, a mechanism is set in the modified Binary Particle Swarm Optimization (BPSO) to randomly select one of them as the solution
The significant and critical information needed for making winter road maintenance decisions is related to road condition and weather data, which is often collected, processed, and transmitted by a road weather information system (RWIS)
Summary
Intelligent Transportation Systems (ITS) are an important part of modern transportation engineering and have a significant impact on society and our everyday life. We are interested in (1) investigating spatiotemporal autocorrelation of RWIS measurements (i.e., road surface temperature (RST)) using large scale case studies, (2) examining the effect of topography and weather severity of regions on spatial and temporal continuity of RST data, and (3) developing an RWIS optimal density chart that can facilitate the decision-making process for planning a long-term RWIS deployment strategy. WSI 1 represents less severe weather regions, followed by moderate, high, and extremely high severe weather zones as WSI 2, WSI 3, and WSI 4, respectively These classification standards will be used to further enhance generalization potentials pertaining to determining optimal RWIS densities for any given region under investigation
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
