Around the world thousands of road weather information systems (RWIS) have been set up to improve road safety during inclement weather (rain, snow, sleet, fog), increase efficient management of labor, equipment and materials for a variety of organizations, and to reduce the adverse environmental impacts from road maintenance activities (salt, de-icing chemicals).
Traditional RWIS for Road Weather Monitoring
Traditional RWIS stations, some of which have been in place for nearly four decades, consist of state-of-the-art road surface and atmospheric sensors, combined with a datalogger. The datalogger compiles information from the sensors, then delivers the often-cryptic piles of data to the end-user for their analysis and interpretation.
In recent years, cameras have become an important addition to traditional RWIS. By being the "eyes on the road," cameras provide users with visual data to support meteorological data received from the sensors. While cameras have greatly improved the usability of RWIS by corroborating data events, there remains room for improvement.
In short, traditional RWIS networks are designed to collect meteorological data about current conditions and deliver that data to the end-user so they can plan road maintenance activities around major weather events. With access to the data collected by the RWIS network, "[r]oad maintenance crews can use this information to decide if road treatment is necessary, the best time to treat, what chemicals or mixtures to use, and how much is required. This will result in reduced road salt usage, thus protecting the environment while at the same time providing significant savings in costs to road maintainers. This information can also contribute to reducing traffic congestion, thereby reducing the levels of greenhouse gases released in the atmosphere by idling cars. By providing timely road information to weather forecasters and maintenance crews, RWIS will play an important role in enhancing safety, efficiency, and sustainability." (Environment and Climate Change Canada, 2017)
A Widening Gap Between Understanding and Application
The Federal Plan for Meteorological Services and Supporting Research study to identify weather information needs for surface transportation, found that "within any given surface transportation sector, all users did not clearly understand how information could make a positive, significant difference in their operations" (National Academies Press, 2004). In other words, the data provided to end-users from traditional RWIS networks has not been as easily understood and interpreted as was expected.
Despite the fact that "total annual global expenditure on the winter maintenance of roads is about $10 billion" (Andrey et al, 2001), we're still seeing adverse impacts of inclement weather, including traffic fatalities remaining high in winter conditions (Kelsall and Redelmeier, 2016), and in Canada, road salts being flagged as high priority substances to be assessed based on their negative environmental impacts. It is continuous public safety and environmental concerns like these that highlight the widening gap between end-user understanding of RWIS data, and how it can be used to improve roadway activities and lessen environmental effects.
Next Generation RWIS Bridges the Analytical Gap
To address these visible gaps in traditional RWIS networks, companies have begun developing new technologies that simplify confirmation of meteorological data and road conditions, provide a more accurate way to forecast and nowcast weather events, and offer a more confident way to make decisions. These next generation RWIS technologies are most often decision support software programs, that can be added-on to existing traditional RWIS networks, or applied to a newly installed, densified network of stations, making them customizable to the end-user's infrastructure and needs.
The technologically progressive next generation RWIS solutions compile data from densified networks of stations, incorporate live data into the analysis, and can even pull in third-party geo-relevant data for a more robust data set. Algorithms then transform this multi-source data into nowcasts and forecasts, which allow end-users to put road maintenance activities into place at exactly the right time to most effectively mitigate the effects of inclement weather.
Some advanced next generation RWIS technologies take it one step further by comparing nowcast and forecast conditions to produce indicators that show end-users precisely when they need to act. These indicators are a form of actionable intelligence that alert end-users to where actions need to be taken in their networks, and more importantly when to take the appropriate action, effectively placing users ahead of impending weather events. Effectively, these solutions take the end user from environmental monitoring, to data analysis, to confident decision-making, all in the time it takes the user to log into their easy-to-use dashboard.
Next generation RWIS technologies are intelligent decision support systems, which provide end-users with more robust data and cues on precisely where and when to take action. The result is saved time, money, and resources. Intelligent decision support will save lives by improving road safety ahead of adverse weather events and reduce harmful operational impacts by letting users know exactly when and where to maintain road ways (no more unnecessary road salting or sanding), even allowing for proactive maintenance activities, such as anti-icing.
As technologies continue to advance, and as awareness of these emerging next generation RWIS technologies continues to grow, we can expect to see a shift from traditional RWIS systems to advanced decision support software tools that have the capability to improve end-user decision-making, reduce maintenance costs, provide greater operational efficiency, and most importantly, increase winter road safety.