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The Pikalert(R) System

Authors: Amanda Siems-Anderson*, National Center for Atmospheric Research, Gerry Wiener, National Center for Atmospheric Research, Seth Linden, National Center for Atmospheric Research, William Petzke, National Center for Atmospheric Research, Thomas Brummet, National Center for Atmospheric Research, Nhi Nguyen, National Center for Atmospheric Research, Padhrig McCarthy, National Center for Atmospheric Research
Topics: Transportation Geography, Climatology and Meteorology, Hazards and Vulnerability
Keywords: road weather, decision support, connected vehicles
Session Type: Paper
Day: 4/9/2020
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual Track 9
Presentation File: No File Uploaded

While improvements to roadway safety and mobility continue to be made, weather remains a major problem for travel along surface roads. In the United States, weather is an over-represented factor in automobile crashes: while adverse weather only occurs about 10% of the year, weather-related crashes account for 21% of the total number of annual crashes. Weather-related delays cause billions of dollars in economic loss as well. It is critical for maintenance departments to strategically apply chemical and plowing operations to maintain safety and mobility, commercial operators to route safely and smartly around and through weather, and everyday drivers to make the best choices of when to delay or reroute their personal trips.

The National Center for Atmospheric Research's Pikalert(R) System combines current and forecast weather conditions with their impacts on the road surface to provide decision support for maintenance and travel. The system, currently used in five states, also takes advantage of connected vehicle technologies to obtain weather observations from vehicles on the roadway. These observations fill in the gaps between traditional weather-observing stations to allow microscale variations along the roadway to be represented. Pikalert also includes a map-based interface for users to display the current and forecast conditions.

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