One of the foremost challenges of fleet management in areas with harsh winters and inclement weather is balancing the safety of your staff with continuing productivity and fleet deployment. Every year winter weather causes millions of dollars in damages, as well as severely impacts regional and national economies.
For fleet managers, having comprehensive and up-to-date weather information is of the utmost importance when navigating winter weather. Current weather forecasting fails to provide the type of hyper-local, real-time road condition data that fleet managers demand. While adequate at providing long-term or broad regional weather trends, weather forecasting still suffers from missing nuances in weather at the local and hyper-local level. It also is difficult to lean on weather forecasts for true road conditions, as what is happening fifty feet above the road may not reflect the conditions on the ground. For fleet managers that rely on accurate and up-to-date weather information, current weather forecasting techniques leave much to be desired.
Internet of Things (IoT) technology is now filling the void left by traditional weather forecasting efforts. With the ability to relay data from the cloud to the end user, and the ability for IoT integrated road weather solutions to extend a broad array of sensor nodes, IoT weather solutions offer enhanced levels of data coverage. IoT sensors are inexpensive compared to traditional weather capture and transmit systems, and can be placed on fleet vehicles and roadside points to grant increased coverage. Alongside increased coverage, an IoT-based weather mapping system also generates real-time data. This allows weather events to be seen and reacted to as they occur. By fusing multiple data sources together, IoT weather solutions also offer more comprehensive ground weather data. Forecasting algorithms can collect and interpret that data and push it to your dashboard in real-time. Taken together, these advances allow IoT systems to provide seamless, real-time, and complete hyper-local data.