By: Mark DeVries
More than 70% of U.S. roads—and nearly 70% of the country’s population—are situated in snowy locales that annually receive an average of more than 5 in. of snowfall. Wintry conditions account for thousands of accidents on our roads that lead to injury or death. Winter road maintenance is a high priority for municipalities and state departments of transportation (DOTs) to keep the roads safe during a storm; in fact, winter road maintenance accounts for about 20% of state DOT budgets.
Consequently, it’s important that agency decision-makers are equipped with the right tools to help them take timely action and overcome the myriad challenges winter weather poses to keep communities safe while reducing road maintenance costs.
The Winter Road Maintenance Evolution
Back in the day, road maintenance decision-makers across the country didn’t have high-tech weather tools at their disposal. In order to determine what might be happening on our roads, we would seek out weather forecasts on the TV or radio, be notified of deteriorating road conditions by local authorities or send staff out to make observations while performing maintenance activities. Consequently, road condition reporting became quickly out-of-date.
That reactive approach meant agencies were starting from a disadvantageous position—roads are already slippery and snow-covered, perhaps there have been accidents and supervisors are calling in maintenance crews from home. More recently, transportation departments have taken steps to get ahead of the storm, whether that has meant loading and sending trucks to wait along their route for the event to ramp up or turning to satellite dishes or daily faxes with forecasts to better understand timing.
Today, winter road maintenance can’t simply be reactive because public expectations are too high. Fortunately, there are now many tools available to us as decision-makers that aid in taking a more proactive approach. With the costs of sensors, communications and power dramatically falling, we’re witnessing an explosion of data. As the number of data sources that agencies want to monitor grows, it can be challenging for managers to gather and interpret all of the necessary information to make the right decision at the right time.
Harnessing the Power of Automation and Machine Learning
In order to effectively plan an agency’s response, decision-makers must consider several factors to keep the roads clear: the kind of storm they are facing and its duration, as well as staff and materials needed.
Automation and machine learning are the newest technologies aiding real-time analysis in winter road maintenance efforts. The tools take disparate data and automatically turn that information into actionable insights, thereby reducing confusion and the need for interpretation while simplifying the decision-making process. By analyzing and visualizing weather data, then combining relevant parameters from all sensors, advanced tools provide meaningful decision-making information to help make timely, targeted decisions more easily, ultimately helping organizations save time and money by optimizing winter maintenance resources.
Knowledge of the current—and future—road conditions is critical to planning and performing winter maintenance operations, and advanced analytics applications provide observation-driven data and forecast inputs in one cloud-based interface to make determinations as simple as possible.
The benefits of incorporating automation into winter road data analysis include the following:
Real-time decision making: Combining accurate real-time measurements with powerful modeling capabilities, analytics tools provide the best situational awareness of current conditions based on the information taken in from radars, mobile sensors, internet of things (IoT) sensors, road weather information systems (RWIS), and environmental sensor stations (ESS), plus a forecast of how the weather will impact road network mobility in the near future. Combined, real-time observations and forecasting provide a much better and more complete picture of the road network and what maintenance efforts are accomplishing to stay ahead of the storm. By getting current and forecasted data, as well as analysis of the data from all of the different sources, to decision-makers instantly, leadership can take actionable steps immediately, thus reducing the chance of a strong storm having a significant impact on travel.
Efficient use of plow operators: When decision-makers understand the storm and its duration, they can better plan maintenance activities. Rather than guessing whether to plow throughout an event or break the crew up to work in shifts, understanding how long crews can plow and the other factors that could affect crew during an event—things like downed limbs, power outages, frozen drains and flooding—helps agencies decide when and where to deploy their fleet and schedule them accordingly.
Meet increasing expectations of the public: Because of social media, people expect to receive relevant news updates almost immediately, and weather is no different. Alternatively, social media also gives the community a quick and very public forum to complain. Quick action to mitigate problem locations and decrease public pressure by meeting the expectations of the community keeps the public better informed and therefore safer.
As we all continue adjusting to the global spread of COVID-19, municipal budgets have been impacted, and decision-makers need to be as efficient as possible. Consequently, making the right decision will be vital, and analytics tools help managers and decision-makers at the city, county and state levels all stay ahead of weather changes and make accurate decisions on when and how to keep roads safer. Leveraging information about current and upcoming weather conditions to determine the level of staffing, materials and road maintenance that might be required eases the decision-making burden of municipalities and transportation departments to make driving more comfortable and safer.
About The Author: DeVries is a solutions manager in transportation with Vaisala.
