Abstract

International audience This paper develops a graph-theoretic framework for large scale bi-dimensional transport networks and provides new insight into the dynamic traffic assignment. Reactive dynamic assignment are deployed to handle the traffic contingencies, traffic uncertainties and traffic congestion. New shortest paths problem in large networks is defined and routes cost calculation is provided. Since mathematical modelling of traffic flow is a keystone in the theory of traffic flow management, and then in the traffic assignment, it is convenient to elaborate a good model of assignment for large scale networks relying on an appropriate model of flow related to very large networks. That is the zone-based optimization of traffic flow model on networks developed by [8], completed and improved by [9]. Document type: Part of book or chapter of book

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.