Modelling Delay Propagation within an Airport Network

This paper is concerned with the propagation of delays within a large network of major airports. As more airports in the United States and in Europe become more congested, it also becomes increasingly likely that delays at one or more airports will spread to other parts of the network. We describe an analytical queuing and network decomposition model developed to study this complex phenomenon. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and, more important, captures the “ripple effect” that leads to the propagation of these delays. The model operates by iterating between its two main components: a queuing engine (QE) that computes delays at individual airports and a delay propagation algorithm (DPA) that updates flight schedules and demand rates at all the airports in the model in response to the local delays computed by the QE. The QE is a stochastic and dynamic queuing model that treats each airport in the network as a M(t)/Ek(t)/1 queuing system. The AND model is very fast computationally, thus making possible the exploration at a macroscopic level of the impacts of a large number of scenarios and policy alternatives on system-wide delays. It has been implemented for a network consisting of the 34 busiest airports in the continental United States. The model provides insights into the complex interactions through which delays propagate through the network and the often-counterintuitive consequences of these interactions. For example, it shows how the propagation of delays tends to “smoothen” daily airport demand profiles, push more demands into late evening hours, and result in local delays which are smaller than what they would have been with the original demand profiles. Such phenomena are especially evident at hub airports. It is also shown that, at hub airports, some flights may benefit considerably (by experiencing reduced delays) from the changes that occur in the scheduled demand profile as a result of delays and delay propagation.