Post-Deployment Analysis of Capacity and Delay Impacts of an Airport Enhancement: Case of a New Runway at Detroit

Abstract

We develop and demonstrate a new statistical method for estimating airport capacity and assessing the capacity and delay impacts of events such as the opening of a new runway, deployment of a new technology, or even transient events such as facility outages. The method is to estimate models of airport throughput using censored regression, recognizing that at a given time the throughput is the minimum of the capacity and available demand. The method is demonstrated for the opening of a new runway, Runway 4L/22R, at Detroit-Wayne County (DTW) Airport. Results show, over the period studied, the main effect of the new runway was to increase departure capacity during VMC conditions. Another finding is that capacity is highly variable, even controlling for visibility condition. Results are then used to estimate arrival and departure delays at DTW, using a simple spreadsheet simulation. We find that simulated delays match observed delays quite well, and the new runway decreased departure delays 15%, while having virtually no effect on arrival delays. Methodologically, we find that our censored regression model accurately depicts operational performance except when protracted periods of low capacity yield a build-up in demand accompanied by low operational counts. To address this, future work should focus on introducing autocorrelation into censored regression models.