CONCEPTUALISATION FOR EVALUATING THE CURRENT RESILIENCE STATUS OF A HUMAN-IN-THE-LOOP CONTROLLER SUPPORT SYSTEM

Currently the resilience in air traffic management is evaluated by comparing the usage of an enhanced or new system to the baseline without this system. Our idea is to assess the resilience of a system without comparing it to a baseline. Therefore, appropriate performance indicators and their thresholds are to be selected to evaluate the current resilience of the system itself in real-time. A system consisting of an air traffic controller support system and the air traffic controller operating it is selected as use case. The paper describes the concept of current resilience and applies it to the use case. To investigate the validity of this approach for the selected use case a dashboard visualizing the necessary parameters is propose

IMPLICATIONS AND BENEFITS OF AIR TRAFFIC CONTROLLERS’ MANUAL ASSESSMENT OF THE SECURITY SITUATION INDICATOR

Validation trials with air traffic controllers followed by a workshop have confirmed, that the Security Situations Indicator provides valuable information at the controller working position. However, the possibility to adjust it by a competent person was proposed. This paper investigates potential implications of manual changes and proposes rules to maximize effectiveness. It outlines the design of the necessary user interaction and information and closes with some visualized examples

Real-Time Calculation and Adaption of Conflict-Free Aircraft Ground Trajectories

To improve the quality of airport surface operations and to pave the way for more autonomous systems, the calculation and adaptation of ground trajectories for aircraft is the backbone for every improvement. These trajectories should be conflict free and easily adaptable to changing conditions in real-time, but on the other hand optimized regarding configurable criteria. This paper describes how this can be achieved using artificial intelligence, especially a multiobjective A* algorithm coupled with a genetic algorithm. The genetic algorithm uses a flexible objective function that can be used to tune the resulting trajectories to the specific needs of the airport/air navigation service provider

GREENER CONFLICT-FREE TAXI TRAJECTORIES USING GENETIC ALGORITHMS

In order to reach the goals of the Paris Agreement, it has become evident that it is necessary to reduce the impact of the whole aviation sector on the environment. The project GreAT (Greener Air Traffic Operations) aims to showcase how a combination of advanced air traffic management tools and procedures for departure, en-route, arrival and surface operations can support this reduction of aviations environmental impact. For the surface operations of aircraft at the airport, the surface management system TraMICS (Traffic Management Intrusion and Compliance System Plus) has been developed to support ground controllers with a security situation assessment and trajectory advisories for taxi operations. TraMICS uses a genetic algorithm to plan and adjust taxi-trajectories in real time to resolve conflicts between aircraft on the ground, with the aim to reduce holding time after engine startup as well as preventable braking and acceleration actions due to other traffic. This paper presents a case study, comparing different configuration profiles for generating conflict-free trajectories using TraMICS and Hamburg airport topology. By using a trajectory configuration profile with higher penalties for holds during the taxi phase, it was possible to create more efficient taxi trajectories with 80 percent fewer holds

VALIDATION OF THE TRAFFIC MANAGEMENT INTRUSION AND COMPLIANCE SYSTEM AS SECURITY-AWARENESS-COMPONENT AT THE CONTROLLER WORKING POSITION

The work of an air traffic ground controller can be affected by various security events. The Traffic Management Intrusion and Compliance System uses possible individual indicators to calculate a Security Situation Indicator that provides an assessment of the current security situation to the controller at their working position. To validate the benefit of the tool, real time simulations were conducted at the Air Traffic Validation Center of DLR. This paper describes the methods, the execution and the results of human-in-the-loop experiments performed with ground and apron controller

A nowcasting model for severe weather events at airport spatial scale: The case study of Milano Malpensa

One of the challenges for meteorologists is to forecast severe weather events developing at small spatial and temporal scales. The H2020 SESAR project "Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM" (SINOPTICA) aims at improving the performances of the numerical weather prediction model to nowcast severe weather events developing in the vicinity of airports. In the project, these new prediction technologies are used to integrate weather events into an Arrival Manager (AMAN) for approach controllers to visualize the actual meteorological development and to support arrival sequencing and target time calculation. We defined the users' requirements through a questionnaire distributed to air traffic controllers to find design solutions for additional controller support system functionalities. We are now developing a nowcasting model for air traffic controller support based on a dense network of ground-based sensors. The focus is on Milano Malpensa airport because it is located in a region with high risk of severe weather development and in which we have an easy availability of high-quality data. The results show that, for this specific case, the use of radar, lightning and Global Navigation Satellite System data greatly improve the prediction of the extremes while the weather stations alone are not essential for this purpose

Il progetto SINOPTICA: come si può migliorare la gestione del traffico aereo durante gli eventi meteorologici severi? (The SINOPTICA project: How can we improve the management of air traffic during severe weather events? )

Project objectives Severe weather cases identification Nowcasting technique and validation Numerical simulations and validation Conclusions and future perspective

A nowcasting algorithm of severe weather events at local spatial scale: The Venezia case study

Nowadays, predicting the exact location and timing of severe convective phenomena at small spatial and temporal scales is still a challenge. In this respect, the H2020 SESAR project "Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM" (SINOPTICA) aims to improve the forecast of severe weather events by using the numerical weather prediction models and the benefit of assimilating non-conventional observations, such as weather radar, GNSS and lightning, in combination with a nowcasting technique to predict the convective cells developing in the vicinity of airports to support air traffic control operations. In this work, we present the results related to the Venice case study pointing out the positive impact of assimilating radar data with lightning and GNSS for a very short-range forecast

Data assimilation and nowcasting for air traffic management purposes: First results from the SINOPTICA H2020 project

One of the main challenges for meteorologists is to improve the prediction of severe weather events that develop on small spatial and temporal scales and that have important repercussions in air traffic management activities (ATM). In this regard, the H2020 project "Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM" (SINOPTICA) aims to demonstrate that numerical weather prediction with high spatial and temporal resolution, benefiting from the assimilation of non-conventional observations, GNSS, weather radar, and lightning data, could improve the prediction of severe weather events for the benefit of air traffic control (ATC) and air traffic management (ATM) in the vicinity of airports. In addition to the numerical simulations, a nowcasting technique called PHAse- diffusion model for STochastic nowcasting (PHAST) has been developed to predict the highly localized convective events triggering in the vicinity of airports and to further support the ATM activities. As part of the project, three severe weather events were identified on the Italian territory which caused the closure of some airport, huge delays on arrivals and departures and numerous diversions. The data of the numerical simulations, carried out with the Weather Research and Forecasting (WRF) and nowcasting technique, performed with PHAST, were integrated into the Arrival Manager 4D-CARMA (4-Dimensional Cooperative Arrival Manager), an adaptive air traffic sequencing and management system for controllers. As part of the project, 4D-CARMA was extended to now generate and optimize 4D trajectories avoiding areas affected by adverse phenomena with the objectives of increasing flight safety and predictability and, under certain circumstances, reducing controllers' and pilots' workload. This work presents the first outcomes of the SINOPTICA project, demonstrating that it is possible to improve the prediction of the above-mentioned events in line with expectations and ATM needs

How can SINOPTICA support ATM and ATC during severe weather events?

The prediction of rapidly developing thunderstorms in small and localized areas is a challenge for the scientific community. Quickly developing but intense thunderstorms are usually characterized by large hail size, huge amount of rain in a short period, high lightning frequency and strong winds thus potentially capable to affect people and socio-economic activities/infrastructures. These phenomena affect also the flight safety, when aircrafts have to fly through or nearby storms, and the aviation management, or triggering flight re-routing, delays or cancellations. Weather-related flight cancellations and delays have increased over the past two decades in the US and Europe and this trend is going to increase due to the human-induced climate change. The objective of the H2020 SESAR Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project is to improve the performances of the numerical weather prediction model to nowcast severe weather events locally developed. In this work, we assimilate different ground based and satellite data into the Weather Research and Forecasting model, we nowcast the severe weather in the surrounding of four airports in Italy and we show the innovative approach to integrate the meteorological results with the Air Traffic Control procedures