Abstract
In order to alleviate flight delay it is important to understand how air traffic congestion evolves or propagates. In this context, this paper focusses on the aggravation of airport congestion by the accumulation of delayed departure flights. We start by applying a heterogeneous network model that takes congestion connection/degree into consideration to predict departure congestion clusters. This is on the basis of the fact that, from a micro perspective, the connection between congestion and discrete clusters can be embodied in models. However, the results show prediction to be of high accuracy and time consuming due to the complexities in capturing the connection in congested flights. The problem of being highly time consuming is resolved in this paper by improving the models by stages. Stage partitioning based on the variation of delay clusters is similar to the typical infectious cycle. For heterogeneous networks the model can describe the congestion propagation and its causes at the different stages of operation. If the connection between flights is homogeneous, the model can describe a more indicative process or trend of congestion propagation. In particular, for single source congestion, the simplified multistage models enable short-term prediction to be fast. Furthermore, for the controllers, the accuracy of prediction using simplified models can be acceptable and the speed on the prediction is significantly increased. The simplified models can help controllers to understand congestion propagation characteristics at different stages of operation, make a fast and short-term prediction of congestion clusters, and facilitate the formulation of traffic control strategies.
Highlights
Airport congestion is an inherent problem in civil aviation, often resulting in substantial departure delays, reroutings, and even cancelations
We focus on the congestion propagation of departure aircraft from airport using multistage and multievent models [32, 33]
This paper builds on our previous work [27, 28], to develop a new congestion propagation model of departure aircraft in multievent and multistage schedule
Summary
Airport congestion is an inherent problem in civil aviation, often resulting in substantial departure delays, reroutings, and even cancelations. Some of the research on delay/congestion propagation focus on the Bayesian network structure learning algorithm by combining genetic algorithms [16,17,18,19,20,21] with timed colored Petri nets [22] Based on these models, simulation tools can be constructed, for example, in the final approach phase to reduce. Cyclic variations in air travel demand and weather at airports have been shown to have an impact on flight delay [26]. These characteristics are the foundation of this paper.
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