Abstract
Inclement weather acutely affects road surface and driving conditions and can negatively impact traffic mobility and safety. Highway authorities have long been using road weather information systems (RWISs) to mitigate the risk of adverse weather on traffic. The data gathered, processed, and disseminated by such systems can improve both the safety of the traveling public as well as the effectiveness of winter road maintenance operations. As the road authorities continue to invest in expanding their existing RWIS networks, there is a growing need to determine the optimal deployment strategies for RWISs. To meet such demand, this study presents an innovative geostatistical approach to quantitatively analyze the spatiotemporal variations of the road weather and surface conditions. With help of constructed semivariograms, this study quantifies and examines both the spatial and temporal coverage of RWIS data. A case study of Alberta, which is one of the leaders in Canada in the use of RWISs, was conducted to indicate the reliability and applicability of the method proposed herein. The findings of this research offer insight for constructing a detailed spatiotemporal RWIS database to manage and deploy different types of RWISs, optimize winter road maintenance resources, and provide timely information on inclement road weather conditions for the traveling public.
Highlights
Inclement weather acutely affects road and driving conditions
It is apparent that an road weather information systems (RWISs) network with better spatiotemporal coverage will generate more timely and reliable estimations
Dey et al [10] provided a comprehensive practice review and found that connected vehicle (CV) can be used as mobile RWISs to enhance route-specific road weather data collection, condition estimation, and traffic management
Summary
Inclement weather acutely affects road and driving conditions. Approximately 22% of vehicle crashes and 25% of total travel time delays are reported to be adverse weatherrelated in the USA [1, 2]. As the predominant sources of road weather data, stationary road weather information systems (RWISs) [6] provide high temporal but limited spatial data coverage. A mobile RWIS [7], which has vehicles collecting road and atmospheric condition information, provides spatially continuous but temporally discrete measurements. Dey et al [10] provided a comprehensive practice review and found that CVs can be used as mobile RWISs to enhance route-specific road weather data collection, condition estimation, and traffic management. Boyce et al [18] developed a road weather condition assessment and forecast system, called Pikalert System It integrates observations from connected vehicles with those from stationary RWIS, radar, and weather model analysis fields. The remainder of this paper is organized into sections: The section looks into the theory behind the spatiotemporal variogram; the Methodology section details the proposed method of this study, including the research procedure, data quality diagnostics, spatiotemporal variogram modelling, and cross validation; the Case Study section deploys the proposed method in the study site to investigate the monthly spatiotemporal correlations; and, the last section discusses the concluding remarks and suggests future work
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
