The United States Department of Transportation (USDOT) Federal Highway Administration (FHWA) initiated the development of the Maintenance Decision Support System (MDSS) in 2001. MDSS provides a single platform, which blends existing road and weather data sources with numerical weather and road condition models in order to provide information on the diagnostic and prognostic state of the atmosphere and roadway (with emphasis on the 1- to 72-hour time period) as well as a decision-support tool for roadway maintenance treatment options. In the past, the system has been used mainly for state departments of transportation for strategic purposes 12-24 hours prior to a storm's arrival in order to prepare the maintenance vehicles and schedule personnel. However, during the 2008–2009 winter season, MDSS was modified and demonstrated over Denver International Airport (DIA), including all six runways, the various ramps and taxiways, and the main arterials leading into and out of the airport. The decisions the airport ground operations and maintenance managers make are extremely complex and different than that of the maintenance supervisors for our normal roadways. Runway maintenance during impactful winter weather events is very expensive and the success of this mission trickles down to aircraft deicing as well as incoming (and outgoing) air traffic. During the first two seasons of deployment of MDSS, the system verified as being reasonably accurate with all of the typical forecast verification measures except for probably the most important metric, usefulness. During the third season of deployment, a 24/7 human element was added to this demonstration and the results were extremely successful. The objective of this presentation is to provide a brief overview of the capabilities of the MDSS system. More importantly, the value (both qualitatively and quantitatively) of the human in the loop of the automated forecast will be discussed.