Does big data help answer big questions? The case of airport catchment areas & competition

We develop algorithms to analyze Information and Communication Technologies (ICT) data in order to estimate individuals' mobility at different spatial scales. Specifically, we apply the algorithms to delineate airport catchment areas in the United Kingdom's Greater London region and to estimate ground access trip times from a very large ICT dataset. The spatial demand is regressed over demographic, socio-economic, airport-specific and ground access modal characteristics in order to determine the drivers of airport demand. Drawing on these insights, we develop a catchment area game inspired by Hotelling that analyzes the potential impact of collaboration between airports and airlines by integrating evidence of consumer behavior with producers' financial data. We apply the game to a case study of two London airports with overlapping catchment areas for local residents. Our assessment of airline-airport vertical collusion and airport-airport horizontal collusion indicates that the former is beneficial to both producers and passengers. In contrast, whilst horizontal and vertical collusion is the equilibrium outcome in the analytic symmetric case, it is found to be less likely in the asymmetric case and the real-world, data-driven analysis, due to catchment area and cost asymmetries. Thus, such new datasets may enable regulators to overcome the long-standing information asymmetry issue that has yet to be resolved. Combining new data sources with traditional consumer surveys may provide more informed insights into both consumers' and producers' actions, which determines the need (or lack thereof) for regulatory intervention in aviation markets.