The installation of road weather information systems (RWIS) has become common practice for both public and private organizations around the globe. But despite RWIS’ capacity to increase safety and efficiency, in many areas we’re still seeing concerning statistics regarding traffic fatalities, resource wastage, and environmental impacts, leaving many questioning, “Is there an issue with the RWIS data itself, or our understanding of it?”
Next generation RWIS technologies are now entering the marketplace, providing a rather simple answer to this very question.
A gap in understanding
Traditional RWIS networks are designed to arm end-users with the meteorological data they need to improve road safety during inclement weather like rain, snow, sleet, fog; manage operational labor, equipment and materials more efficiently; and reduce any adverse environmental impacts from road maintenance activities that use salt and de-icing chemicals.
However, a Federal Plan for Meteorological Services and Supporting Research study aimed at identifying weather information needs for surface transportation found that 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, according to data from 2001, we’re still seeing adverse impacts of inclement weather. Traffic fatalities still remain high in winter conditions, and in Canada, road salts have been flagged as a high priority substance requiring an assessment of their negative environmental impacts.
It’s 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 optimize roadway activities and lessen environmental effects.
Bridging the analytical gap
To address these visible gaps in traditional RWIS networks, companies are 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.
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 dataset. 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 and where they need to act. 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 roadways — no more unnecessary road salting or sanding — and 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.