Javascript must be enabled to continue!
Air Traffic Management during Rare Events Such as a Pandemic: Paris Charles de Gaulle Case Study
View through CrossRef
Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.2 million passengers. Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals. With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry. More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic. Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed. In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side. We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace. We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios. An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side. The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic). When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts; however, it reveals the need for additional runways.
Title: Air Traffic Management during Rare Events Such as a Pandemic: Paris Charles de Gaulle Case Study
Description:
Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.
2 million passengers.
Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals.
With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry.
More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic.
Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed.
In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side.
We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace.
We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios.
An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side.
The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic).
When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts; however, it reveals the need for additional runways.
Related Results
Hydatid Disease of The Brain Parenchyma: A Systematic Review
Hydatid Disease of The Brain Parenchyma: A Systematic Review
Abstarct
Introduction
Isolated brain hydatid disease (BHD) is an extremely rare form of echinococcosis. A prompt and timely diagnosis is a crucial step in disease management. This ...
Breast Carcinoma within Fibroadenoma: A Systematic Review
Breast Carcinoma within Fibroadenoma: A Systematic Review
Abstract
Introduction
Fibroadenoma is the most common benign breast lesion; however, it carries a potential risk of malignant transformation. This systematic review provides an ove...
Aviation English - A global perspective: analysis, teaching, assessment
Aviation English - A global perspective: analysis, teaching, assessment
This e-book brings together 13 chapters written by aviation English researchers and practitioners settled in six different countries, representing institutions and universities fro...
TYPES OF AI ALGORİTHMS USED İN TRAFFİC FLOW PREDİCTİON
TYPES OF AI ALGORİTHMS USED İN TRAFFİC FLOW PREDİCTİON
The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic...
Traffic Prediction in 5G Networks Using Machine Learning
Traffic Prediction in 5G Networks Using Machine Learning
The advent of 5G technology promises a paradigm shift in the realm of
telecommunications, offering unprecedented speeds and connectivity. However, the
...
MODELİNG OF TRAFFİC LİGHT CONTROL SYSTEMS
MODELİNG OF TRAFFİC LİGHT CONTROL SYSTEMS
Traffic light control systems are commonly utilized to monitor and manage the flow of autos across multiple road intersections. Since traffic jams are ubiquitous in daily life, A c...
SMART TRAFFIC MANAGEMENT SYSTEM
SMART TRAFFIC MANAGEMENT SYSTEM
ABSTRACT
In C++, a traffic management system is a software program that simulates and regulates traffic flow. It creates simulations of automobiles, traffic lights, and road inters...
A Traffic Flow Prediction Method Based on Blockchain and Federated Learning
A Traffic Flow Prediction Method Based on Blockchain and Federated Learning
Abstract
Traffic flow prediction is the an important issue in the field of intelligent transportation, and real-time and accurate traffic flow prediction plays a crucial ro...

