About the research
State agencies spend tens of millions of dollars on winter maintenance each year. For example, the State of Indiana spends upwards of $60M annually on salt, fuel, labor, equipment, and other maintenance costs, so it is imperative to make data-driven decisions. Advanced crowdsourced connected vehicle data have emerged in the past eight years that can leverage beyond weather data, vehicle dynamics data from engine output, and drivetrain and wheel sensors. Micro-slippage and roadway friction can be estimated at 75-ft segments of roadway aggregated at a 10-minute frequency without any additional instrumentation, from consumer vehicles off the production line. Traditionally, agencies have leveraged RWIS and other road weather sensors for tactical decision-making, but they are expensive to deploy and maintain and can only provide limited spatial coverage. Agency maintenance vehicles such as snowplows run on dedicated routes, and the timing of deployments, as well as the length and duration of routes, may not be representative of general traffic behavior. A crowdsourced solution provides more agile and broader network coverage because of the moving nature of vehicles.
This research includes a set of tasks to evaluate commercially available connected vehicle (CV) data to measure friction, wet-state, and ambient temperature over large road networks across multiple states. Static RWIS can be used for groundtruth if CV data is gathered in the proximity. If the new data source is found to be usable, a contingency plan on how these data can be integrated into the existing datasets, decision-making systems, and business processes will also be developed.