Print Email Facebook Twitter Modelling delay propagation within an airport network Title Modelling delay propagation within an airport network Author Pyrgiotis, N. Malone, K.M. Odoni, A. Publication year 2013 Abstract We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and 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 applied to a network consisting of the 34 busiest airports in the continental United States and provides insights into the interactions through which delays propagate through the network and the often-counterintuitive consequences. Delay propagation tends to " smoothen" daily airport demand profiles and push more demands into late evening hours. Such phenomena are especially evident at hub airports, where 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. © 2011 Elsevier Ltd. Subject OrganisationSMb - Smart MobilityBSS - Behavioural and Societal SciencesReliable Mobility SystemsTrafficMobilityAirport delaysDelay propagationNetwork of airportsAirport delaysAirport networkDelay propagationDemand ratesFlight schedulesHub airportsLarge networksLocal delaysMacroscopic levelsNetwork decompositionNetwork delaysQueuing modelsQueuing systemsRipple effectsScheduled demandsComplex networksQueueing networksAirportsairportalgorithmnetwork analysisnumerical modelstochasticitytraffic congestion To reference this document use: http://resolver.tudelft.nl/uuid:53ed1c4c-b91d-4ae5-98a3-4172dc1b9c21 TNO identifier 471496 ISSN 0968-090X Source Transportation Research Part C: Emerging Technologies, 27, 60-75 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.