Airport capacity has become a constraint in the air transportation networks, due to the growth of air traffic demand and the lack of resources able to accommodate this demand. This paper presents the algorithmic implementations of a decision support system for making a more efficient use of the airspace and ground capacity. The system would be able to provide support for air traffic controllers in handling large amount of flights while reducing to a minimum the potential conflicts. In this framework, airspace together with ground airport operations are considered. Conflicts are defined as separation minima violation between aircraft for what concerns airspace and runways, and as capacity overloads for taxiway network and terminals. The methodology proposed in this work consists of an iterative approach that couples optimization and simulation to find solutions that are resilient to perturbations due to the uncertainty present in different phases of the arrival and departure process. An optimization model was employed to find a (sub)optimal solution while a discrete event-based simulation model evaluated the objective function. By coupling simulation with optimization, we generate more robust solutions resilient to variability in the operations, this is supported by a case study of Paris Charles de Gaulle Airport.
We present a novel anomaly-based detection approach capable of detecting botnet Command and Control traffic in an enterprise network by estimating the trustworthiness of the traffic destinations. A traffic flow is classified as anomalous if its destination identifier does not origin from: human input, prior traffic from a trusted destination, or a defined set of legitimate applications. This allows for real-time detection of diverse types of Command and Control traffic. The detection approach and its accuracy are evaluated by experiments in a controlled environment.
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
The main challenge addressed in FTMAAS (Freight Traffic Management As A Service) is the integration of logistics and traffic management information. Digitalization is progressing quickly in both areas, but operational connections and synergies are scarce. The mission of the FTMAAS Living Lab is to connect these two subsystems by developing, implementing and testing integrating software applications that benefit both worlds. The Living Lab focuses on the International Freight Corridor South (Rotterdam-Venlo) and manages 3 main running cases and 6 research subprojects. Research focuses on questions of value creation, analytics and optimization of both logistics and network level traffic management.
The main challenge addressed in FTMAAS (Freight Traffic Management As A Service) is the integration of logistics and traffic management information. Digitalization is progressing quickly in both areas, but operational connections and synergies are scarce. The mission of the FTMAAS Living Lab is to connect these two subsystems by developing, implementing and testing integrating software applications that benefit both worlds. The Living Lab focuses on the International Freight Corridor South (Rotterdam-Venlo) and manages 3 main running cases and 6 research subprojects. Research focuses on questions of value creation, analytics and optimization of both logistics and network level traffic management.
Dankzij digitalisering maken logistieke processen een efficiencyslag door. Een specifieke groep van toepassingen bevindt zich in de afstemming tussen logistiek en verkeer. Enerzijds kunnen logistieke planning en –uitvoering op verkeersomstandigheden worden geoptimaliseerd; anderzijds kan verkeersmanagement baat hebben bij informatie over transportprocessen. Tot nu toe ontwikkelen deze werelden zich gescheiden, en ontbreekt het aan een programma waarin systematisch alle raakvlakken worden verkend en kansrijke applicaties worden ontwikkeld. Het programma brengt deze werelden bijeen en ontwikkelt het concept van “verkeersmanagement voor goederenvervoer als dienst” zodat kansrijke toepassingen en de voorwaarden voor realisatie in beeld komen.
