Although no hard data regarding the issue of runway performance exists, it is currently suspected that airplanes are spending an excessive amount of time taxiing on runways after landing. This reduces the number of airplanes that can land in a given time period, which in turn limits the amount of profit airlines and airports can generate. As a contractor for the Federal Aviation Administration, it is the job of Northrop Grumman to address this issue. For this reason, the team is developing a machine learning system which can be used to gather data surrounding the problem of excessive runway occupancy time. In addition to this, the team is also creating a second program which can identify the optimal runway exit airplanes ought to use which would limit the costs associated with runway occupancy time and taxi time. As a result of this study, current runway performance is now quantitatively understood, and the factors which affect runway performance are now visible.