While typical weather forecasts cover a large area, they often do not reflect the actual conditions on the road. Since the early 2000s road weather technologies have significantly improved the capacity for enterprises and government agencies to predict road weather conditions, but now the next evolution of road weather solutions is here to provide hyper-local data with much improved accuracy, scalability and cost effectiveness.
Road Weather 1.0
Every year, state and local governments spend billions of dollars on snow removal during the winter season. In the early 2000s, the Maintenance Decision Support System (MDSS) was developed by the National Center for Atmospheric Research (NCAR) to solve the problem of inadequate weather information for agencies that maintain and clear snow from the roads during inclement winter weather. Today, government agencies (international, state and local) have adopted this technology and have implemented MDSS within their year-round road maintenance and wintertime snow-removal operations.
The commercialization of Road Weather Information System (RWIS) stations to build up tactical observation networks in critical spots over federal and state roadways fueled the development of MDSS deployments. RWIS are relatively expensive fixed stations that include scientific-grade weather instrumentation, which have been installed over specific locations. RWIS stations combine technologies to collect, transmit and disseminate weather and road condition information.
Although RWIS has become the standard for measuring road surface conditions, federal and state government transportation budgets are not accommodating the observation density needed within state highways and road segments to fundamentally improve road condition situational awareness and treatment methods. This gap in capability has fostered the development of new mobile sensing technologies and instrumentation that can be installed directly on fleet vehicles.
These new mobile sensing techniques are being deployed by state Departments of Transportation (DOTs), creating a new capability to calibrate and tune pavement forecast models. This revolutionary observation network is invaluable for optimising road surface treatment tools and pavement forecast models, as well as for auto and truck manufacturers, in order to better manage poor road conditions at various speeds.
While MDSS, RWIS and mobile sensing technologies have served to revolutionize the way roads are maintained during adverse winter weather, newer technologies have emerged that will likely lead to the next revolution in road weather forecasting as it affects maintenance and travel on the world’s roadways.
Road Weather 2.0
Since MDSS was first introduced at NCAR in the early 2000s, the vertical compute platform (supercomputer) has fully transformed into highly distributed cloud service architecture with Graphics Processing Unit (GPU) enabled compute power that revolutionizes big data management and deep learning analytics.
Scalable statistically-based road weather forecast systems are now available that can be included in an MDSS system to provide more accurate and highly-scalable road weather information to end-users. The days of forecasts being constrained to RWIS locations that are tens to hundreds of miles apart are behind us. The new high-resolution, scalable road weather forecasts will eliminate the guesswork between RWIS locations allowing for much more efficient use of resources and materials when adverse weather affects operations.
Similarly, as the Internet of Things (IoT) remote sensing wave has hit the market over the past several years, the time has arrived for much lower-cost and scalable fixed observation solutions. Commercial-grade instrumentation is now available at a fraction of the cost of standard RWIS stations, allowing for a much denser fixed station network. Investment by states and local governments in this new way of building out observation networks along roadways will not only save money within tightening budgets, but will actually provide a more robust snapshot of what is happening to the roads during impactful weather events.
Since most agency fleets are now connected, lower cost and self-contained mobile sensors can take advantage of wide-spread cellular communication networks accessible along most major routes. Sensors are now available that cost significantly less than typical mobile sensors, and can be deployed on any type of vehicle, both public and private. A deployment on 20,000 – 25,000 coast-to-coast long-haul trucks, for example, would cover 90 percent of US interstates, with observational measurements of each part of the road coming in approximately every thirty minutes.
Transforming Transportation, Maintenance and Driver Safety
All of these emergent technologies are ready to transform transportation, maintenance and driver safety. Solutions like Fathym’s surface weather forecast provide the relevant information needed to support the driver’s changing decisions as the weather becomes impactful. Information about slick roads, low visibility, high crosswinds, lightning, floods and tornadoes is available now and can be integrated into connected vehicles.
Fathym offers a statistically-based, machine learning road weather forecast that is dynamically-generated, globally-scaled, and enables sub-kilometer forecasts, alerts and weather risk variables. Route and geographic point forecasts are available via API.
Fathym is currently utilizing its forecasting solution with the Alaska Department of Transportation to make the roads of Alaska safer. The Alaska DoT is now able to make better, hyper-local decisions on when and where to deploy road crews and treat roads with anti-icing chemicals.