By Rachel Adams, Contributing Author
From increased driver expectations surrounding safety and the rise in extreme weather events, to the application of de-icing materials and the timely removal of snow and ice, agencies responsible for road maintenance during the winter months face several unique challenges.
Wintry weather is responsible for more than 1.2 million crashes in the U.S. every year, and more than 70% of American roads are located in regions that annually receive more than 5 inches of snowfall on average. Consequently, winter road maintenance operations are critically important to our nation’s roadway safety and mobility.
And when it comes to winter maintenance, better data saves lives. Road maintenance teams need to know when the next winter storm will hit and which roadways will be most impacted. To effectively perform maintenance operations that reduce the risks posed by hazardous winter driving conditions, it is critical that road authority decision-makers are equipped with the right tools to monitor the current conditions on roadways and take timely action aimed at keeping communities safe while reducing the high costs of winter road maintenance.
From Reactive to Proactive Road Maintenance
Winter road maintenance decision-making is complex. Agencies responsible for maintaining our roads during the winter months need to consider a multitude of factors in order to effectively clear roads and protect drivers and passengers from hazardous conditions.
First, the storm and its duration; What kind of storm is coming? How intense will it be? How long will it last? Understanding the storm’s conditions and how surface temperatures will react before, during, and after the event is critical in deciding the optimal approach for mitigating its impact. With this information, decision-makers can better address the next two factors: crew and materials. Knowing when and where to deploy fleets, the correct maintenance actions to take, the amount of materials available, and how effective they will be given the predicted conditions is essential to prioritizing maintenance operations.
In the past, road maintenance agencies relied on general forecasts and government requests to make the roads safer during storms. When a winter storm hit, most agencies reacted well after the event began and were often notified of deteriorating conditions by law enforcement or a simple alarm. Taking this reactive approach, however, puts road agencies in a difficult spot as roads are already slushy, snowy, or icy, accidents have likely already occurred, and maintenance crews are behind. In today’s world, winter road maintenance can’t be just reactive because public safety expectations are too high.
As level-of-service expectations increase, agencies are demanding better forecasting and weather tools to become more proactive and get ahead of weather events. Today, there are many more tools and sources of information that help road crews enjoy the peace of mind that comes with a sophisticated network of sensors that proactively alert maintenance crews to extreme weather conditions, through both observations and forecasts.
Making Winter Road Maintenance “Smarter”
Winter road instrumentation is evolving to include comprehensive high-tech weather tools that allow for nowcasting and forecasting. The rise of the Internet of Things — enabled by low-cost sensor and power technologies — has led to more devices collecting data out in the real world.
Whether coming from radars, mobile sensors, IoT sensors, road weather information systems, or environmental sensor stations, specific information about road networks or road weather is helpful in planning maintenance activities. While more data is generally better, this data explosion can inundate road maintenance agencies and make it challenging for them to quickly interpret massive amounts of data in order to make the right decision at the right time.
As a result, effectively planning an agency’s response requires tools to help predict the next move a storm will make. The planning and execution of winter maintenance operations depend on accurate, reliable knowledge of the current, and future, road and weather conditions.
Combining Observations and Forecasts
Weather forecasts are concerned with what is happening in the air, while road weather forecasts are interested in what is happening on the road surface. Combining real-world observations with forecasting to deliver actionable information drives the best decision-making when it comes to winter road maintenance activities.
The starting point for analyzing and predicting road weather conditions is the atmospheric weather forecast. There are five steps involved in building a hyperlocal atmospheric weather forecast. First, measurements are taken around the world to know the current atmospheric conditions. Next, global weather forecasts are made at the world’s weather prediction centers. High resolution models are then used to refine the global weather models for the local environment. From there, the forecasts are enhanced using artificial intelligence that learns from previous measurements, forecasts, and forecast errors. Finally, a nowcast model is generated using the latest weather station, radar and satellite data to improve the initial short-range forecast.
The atmospheric forecast is an important input to a road weather forecast system, which consists of an energy balance model, together with a material balance model and local data. The energy balance model predicts the surface and subsurface temperatures, while the material balance model tracks and categorizes the amount and state of water and chemicals on the road surface. The forecasts are then adjusted according to the local data, such as nearby environmental factors, presence of bridges, traffic profiles and the impact of shading. The road weather model predicts surface temperature and surface condition by taking multiple factors into account, including the following:
- Road surface temperature (solar radiation, traffic heating/turbulence, radiative cooling, de-icing).
- The presence of water, snow, ice, and chemicals (rain, snow, condensation or frost, evaporation or sublimation, treatment and snow removal, traffic spray).
- Road weather condition (dry, moist, wet, slush, snow, frost, ice, black ice).
With access to measurements from fixed road weather stations and mobile detectors, the road weather forecast system continually fine-tunes and verifies the forecast — immediately reacting to the latest observed situation on the road network — for even greater accuracy. Real-time measurements improve predictions and support actionable monitoring to keep roads safe.
The Benefits of Optimizing Road Maintenance Decision-Making
Combining field observations and forecasting provides a clear and accurate view of current and near-future road weather conditions. This integrated approach helps reduce confusion and the need for interpretation, ultimately simplifying road maintenance decision-making for greater speed, accuracy, and proactive maintenance.
Through a combination of automation, machine learning, and advanced analytics applications, new tools ingest measurements and forecasts and automatically convert that information into actionable, observation-driven insights to help make timely, targeted decisions more easily. By analyzing and visualizing atmospheric and road weather data, then combining relevant parameters from all sensors and forecasts, road maintenance innovations provide meaningful information that helps organizations plan ahead with confidence and save time and money through the optimization of winter maintenance resources.
The benefits of leveraging tools that help you stay ahead of weather changes and make accurate decisions on when — and how — to keep your roads safer include, ensured mobility, reliable journey times, improved sustainability of maintenance operations, efficient treatment practices, and a consistent level of service.
With access to all of the information — current conditions, precipitation levels, and near-term forecast data — decision-makers are well-equipped to decide when and where to deploy fleets. Comprehensive weather hazard information systems empower authorities to maintain safer roads. Since such tools analyze and visualize data from fixed RWISs/ESSs, IoT, and mobile sensors, it is simple to select and combine relevant parameters from all sensors to provide required information for quick decision-making.
The combination of accurate measurements and powerful modeling provides the best situational awareness of current conditions, helping decision-makers prioritize treatments, optimize salt and liquid usage, and efficiently plan equipment resources to minimize any negative environmental impact. Efficient treatment practices help agencies optimize winter maintenance resources, thereby saving time and money.
Finally, by removing the inconsistencies inherent in decision-making that relies on the interpretation of different data sets, solutions that aggregate and analyze weather and road weather information provide decision-makers with a clear picture of the current situation. This insight enables operators and supervisors to deliver consistent levels of service that meet ever-increasing public safety expectations.
With the arrival of new technologies and practices, winter road maintenance is evolving to improve efficiency and maintenance outcomes. By harnessing technological innovation that delivers an observation-driven approach, integrating real-time measurements with analysis and visualization, road agencies are better equipped to stay ahead of weather impacts. In doing so, timely and targeted decisions about when and where to deploy resources becomes more easily achieved, ensuring road mobility and environmental sustainability while meeting budgetary and performance metrics and increasing safety. RB
Rachel Adams, Head of Winter Maintenance at Vaisala, has more than 25 years’ experience in understanding the impact of weather on the roadway. Rachel is an Environmental Science and Geography honors graduate, from the University of Bradford, England with a post-graduate diploma in Marketing.
