A Data-Driven Method for Determining Hotspots in Airport Movement Areas

As aviation demand continues to increase in the post-COVID-19 era, the risk of incidents and accidents in aircraft operations at airport airfields has increased. Large hub airports often contain multiple runways connected to the apron area using a complex system of taxiways. As a result, an increased risk of incidents has been appearing in certain parts of these complex taxiway systems, which are defined as airport taxiway hotspots. While a list of taxiway hotspots at U.S. airports exists to support and alleviate concerns for pilots and ground personnel, the methodology for determining hotspot locations is not transparent, and it limits the aviation industry’s ability to evaluate and refine safety practices. Existing safety measures rely on reactive approaches based on prior incidents and accidents. While the overall accident rate in aviation is significantly lower than in other modes of transport, the severe consequences of aviation accidents necessitate robust analytical methods to predict, identify, and mitigate potential risks. This research develops an improved method for systematically identifying quantifiable airfield taxiway hotspots, allowing areas of concern to be addressed before taxiways are constructed. Existing taxiway hotspots can be successfully identified using metrics such as aircraft traffic density, speed, airfield geometry, and number of operations. This study applies the proposed theoretical taxiway hotspot identification model to Chicago’s O’Hare International Airport and Atlanta’s Hartsfield-Jackson International Airport. By providing a transparent and quantifiable set of metrics for taxiway hotspot analysis and detection, this study offers additional means to improve operational procedures and advance future airport airfield designs.