About the research
Until recently, road weather information system (RWIS) stations have been overlooked and underutilized by many transportation agencies and maintenance personnel. It may stem from the fact that the data is point measurements with large spatial gaps in between, resulting in an incomplete view of road surface conditions (RSC) over the entire highway network. Also, one of the most important pieces about RSC, namely, snow coverage or bare pavement status, which is available from RWIS cameras or fleet cameras, is only accessible manually by maintenance personnel or road users. Therefore, there is a need to automate the recognition of snow coverage via images from RWIS or other data sources such as fleet dash cameras or traffic cameras, improve RSC inferences at unmeasured locations, and generate various RSC data and performance measures that can be used by maintenance personnel and road users.
The primary objective of this project is to continue the previous research efforts on developing highly transferrable and universally applicable methodologies, models, and tools for visualizing and inferring road surface conditions using data from RWIS and other road condition monitoring systems.
Ultimately, this project will provide winter maintenance personnel with newfound knowledge and analytical tools, of which they can employ to make better use of available resources, resulting in better maintenance and improvements to their highway infrastructure thereby promoting improved winter mobility and safety.