Optimal RWIS Sensor Density and Location – Phase II_TPF-5(290)

(2020) Optimal RWIS Sensor Density and Location – Phase II_TPF-5(290). Transportation, Department of

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Abstract

Preventing weather-related crashes is a significant part of maintaining the safety and mobility of the travelling public during winter months. A road weather information system (RWIS) is a combination of advanced technologies that collect, process, and disseminate road weather and condition information. This information is used by road maintenance authorities to make operative decisions that improve safety and mobility during inclement weather events. Many North American transportation agencies have invested millions of dollars to deploy RWIS stations to improve the monitoring coverage of winter road surface conditions. However, the significant costs of these systems motivate governments to develop a framework to optimize the spatial design of the RWIS network. The design of these networks often varies by region, and it remains an unresolved question what should be the optimal density and location of an RWIS network to provide adequate monitoring coverage of a given region. To fill this gap, this project aimed to develop a methodology for optimizing the density and location of an RWIS network for a given region based on its topographic and weather characteristics. A series of geostatistical spatiotemporal semivariogram models were constructed and compared using topographic position index (TPI) and weather severity index (WSI) to measure relative topographic variation and weather severity, respectively. Specifically, this project considered the nature of spatiotemporally varying RWIS measurements by integrating larger case studies and examining two analysis domains: space and time. The study area captured varying environmental characteristics, including regions with flatland or varied terrain and different severities of winter weather. The optimal RWIS density and location for different topographic and weather severity regions were determined using spatiotemporal semivariogram parameters. Output of this study revealed a strong dependency of optimal RWIS density on topographic and weather characteristics of a region. Moreover, this study suggests that RWIS data collected from a specific region can be used to estimate the number of stations required for regions with similar zonal characteristics. The proposed method will provide decision-makers with a tool they need to develop a long-term RWIS implementation plan.