Frameworks for Adaptive Airport Planning and Techniques for a New Era of Planning

Abstract

Airport planners, in determining the long-term development of an airport's infrastructure, estimate future use of the airport by using two loose categories of methods: ( a) peer group learning and ( b) forecasting. In peer group learning, airport planners compare their airports with like airports and engage in peer-to-peer exchange of information about lessons learned from past experiences and technical and planning guidance. The blend of quantitative and qualitative methodologies can be a powerful tool for airport planners because the planners can validate their forecasts with the actual experiences at similar airports. In the current state of the aviation industry, airport planners must engage in peer group learning because the environment in which they are planning their airports is more volatile than it was previously; however, while forecasting has been well studied by scholars and airport planners, peer group learning has attracted relatively less attention. Given the role of peer group learning in airport planning, airport planners must pay careful attention to how they define their peers in ever-evolving economic and industrial environments. Since deregulation of the airline industry in the 1970s, the airport system has become much more nuanced and is no longer easily defined, while airport planners often resort to single metrics such as enplanements as a peer criterion. This study developed and tested a peer identification methodology that reflects volatilities in the economy and the airport industry by using an expanded list of both static and dynamic metrics. This methodology highlights an important lesson that metrics matter in the ways in which airports identify their peers, benchmark their performance, and help to improve their plans.