Congestion Charging at Airports: Dealing with an Inherent Complexity

Air transport delays have increased during the past decade both in Europe and US. The main reasons have been continuously growing air transport demand, constrained capacity of the system infrastructure, and disruption of services caused by bad weather, failures of the infrastructure components, facilities and equipment, and industrial action of the aviation staff. Growing demand combined with constrained infrastructure capacity and frequently disrupted by a bad weather have been identified as the most frequent causes of congestion and delays at large airports. Potential remedies have shown to be improvement of utilisation of the existing infrastructure capacity by introducing technological and operational innovations, physical expansion of this capacity, and management of access to the scarce infrastructure capacity. The first option has shown to have the very limited effect. In many cases, the second option has been impossible to be implemented due to the various political and environmental constraints in terms of land take in both the airport airside and landside area. The solution of management of access to the existing scarce infrastructure capacity (i.e., ?demand management) has recently been considered as an additional viable option to relief the congestion problem. This paper deals with modelling of management of access to a congested airport by using congestion charging. The complexity of setting up a relevant congestion charge (toll) based on the system delays imposed by an additional aircraft on itself and on the others during peak-periods has been particularly elaborated. For such a purpose, queuing model of a congested airport, as an essential tool for estimating the aircraft delays, particularly in cases of changes of the airport capacity caused by disruptions, is developed. The existing theory of congestion pricing is upgraded by an explicit introduction of the differences between particular customers - the aircraft - requesting service during peaks in terms of the arrival time, type (seat capacity, operational cost), type of flight (short, medium, long-haul), and actual number of passengers on board. These all are also the most important factors expected to influence a tool, which, if consequently applied, is expected to change in line with changes of these factors. This also illustrates controversy and complexity of implementing such tool, the process, which is under consideration (i.e., in a rudimentary phase) at some airports in the U.S. and Europe. KEY WORDS: air transport, airport, queuing theory, congestion pricing