Reroute Prediction Service

The cost of delays was estimated as 33 billion US dollars only in 2019 for the US National Airspace System, a peak value following a growth trend in past years. Aiming to address this huge inefficiency, we designed and developed a novel Data Analytics and Machine Learning system, which aims at reducing delays by proactively supporting re-routing decisions. Given a time interval up to a few days in the future, the system predicts if a reroute advisory for a certain Air Route Traffic Control Center or for a certain advisory identifier will be issued, which may impact the pertinent routes. To deliver such predictions, the system uses historical reroute data, collected from the System Wide Information Management (SWIM) data services provided by the FAA, and weather data, provided by the US National Centers for Environmental Prediction (NCEP). The data is huge in volume, and has many items streamed at high velocity, uncorrelated and noisy. The system continuously processes the incoming raw data and makes it available for the next step where an interim data store is created and adaptively maintained for efficient query processing. The resulting data is fed into an array of ML algorithms, which compete for higher accuracy. The best performing algorithm is used in the final prediction, generating the final results. Mean accuracy values higher than 90% were obtained in our experiments with this system. Our algorithm divides the area of interest in units of aggregation and uses temporal series of the aggregate measures of weather forecast parameters in each geographical unit, in order to detect correlations with reroutes and where they will most likely occur. Aiming at practical application, the system is formed by a number of microservices, which are deployed in the cloud, making the system distributed, scalable and highly available.Comment: Submitted to the 2023 IEEE/AIAA Digital Aviation Systems Conference (DASC

Big data-driven prediction of airspace congestion

Air Navigation Service Providers (ANSP) worldwide have been making a considerable effort for the development of a better method to measure and predict aircraft counts within a particular airspace, also referred to as airspace density. An accurate measurement and prediction of airspace density is crucial for a better managed airspace, both strategically and tactically, yielding a higher level of automation and thereby reducing the air traffic controller's workload. Although the prior approaches have been able to address the problem to some extent, data management and query processing of ever-increasing vast volume of air traffic data at high rates, for various analytics purposes such as predicting aircraft counts, still remains a challenge especially when only linear prediction models are used. In this paper, we present a novel data management and prediction system that accurately predicts aircraft counts for a particular airspace sector within the National Airspace System (NAS). The incoming Traffic Flow Management (TFM) data is streaming, big, uncorrelated and noisy. In the preprocessing step, the system continuously processes the incoming raw data, reduces it to a compact size, and stores it in a NoSQL database, where it makes the data available for efficient query processing. In the prediction step, the system learns from historical trajectories and uses their segments to collect key features such as sector boundary crossings, weather parameters, and other air traffic data. The features are fed into various regression models, including linear, non-linear and ensemble models, and the best performing model is used for prediction. Evaluation on an extensive set of real track, weather, and air traffic data including boundary crossings in the U.S. verify that our system efficiently and accurately predicts aircraft counts in each airspace sector.Comment: Submitted to the 2023 IEEE/AIAA Digital Aviation Systems Conference (DASC

Safety Analysis Methods for Complex Systems in Aviation

Each new concept of operation and equipment generation in aviation becomes more automated, integrated and interconnected. In the case of Unmanned Aircraft Systems (UAS), this evolution allows drastically decreasing aircraft weight and operational cost, but these benefits are also realized in highly automated manned aircraft and ground Air Traffic Control (ATC) systems. The downside of these advances is overwhelmingly more complex software and hardware, making it harder to identify potential failure paths. Although there are mandatory certification processes based on broadly accepted standards, such as ARP4754 and its family, ESARR 4 and others, these standards do not allow proof or disproof of safety of disruptive technology changes, such as GBAS Precision Approaches, Autonomous UAS, aircraft self-separation and others. In order to leverage the introduction of such concepts, it is necessary to develop solid knowledge on the foundations of safety in complex systems and use this knowledge to elaborate sound demonstrations of either safety or unsafety of new system designs. These demonstrations at early design stages will help reducing costs both on development of new technology as well as reducing the risk of such technology causing accidents when in use. This paper presents some safety analysis methods which are not in the industry standards but which we identify as having benefits for analyzing safety of advanced technological concepts in aviation

Risk analysis of the airborne time-based spacing operation through a stochastically and dinamiclly coloured Petri net model.

A segurança do espaço aéreo pode aumentar consideravelmente com o uso de operações de espaçamento e separação aerotransportados. Sob este paradigma, a tarefa de manter distância em relação a outras aeronaves é delegada aos pilotos, que contarão com o Sistema de Assistência de Separação Aerotransportado (ASAS). Com este sistema, ainda em fase experimental, os pilotos tornam-se cientes dos riscos do tráfego circundante com até 15 minutos de antecedência, sem necessitar de auxílio dos controladores de tráfego aéreo. Esta antecedência é muito maior que a do atual sistema anti-colisão (TCAS), que é de menos de 1 minuto. O sistema ASAS utiliza uma tecnologia de comunicação mais avançada que a tecnologia do transponder modo C, utilizado atualmente pelo sistema anti-colisão. O novo sistema ASAS está sendo desenvolvido intensivamente no Eurocontrol e em outras iniciativas nos Estados Unidos da América, e funcionará em conjunto com o atual sistema anti-colisão, proporcionando redundância, ou seja: se o antigo sistema falhar, o novo ainda pode emitir um alerta, ou vice-versa. O presente trabalho de pesquisa aborda a aplicação do ASAS para aumentar a precisão do espaçamento entre aeronaves que chegam sequencialmente a um determinado aeroporto, por meio de um formalismo matemático denominado \"Rede de Petri Estocástica e Dinamicamente Colorida\", com a obtenção de dados quantitativos sobre o risco de acidente. Esses dados indicam que o risco de acidente é significativamente menor com o uso do ASAS do que sem o uso do ASAS.The safety in the airspace can considerably increase with the use of airborne spacing and separation operations. Under this paradigm, the task of maintaining a safe distance between aircraft is delegated to the pilots, which will be supported by the Airborne Separation Assistance System (ASAS). With this system, which is still in experimental phase, pilots become aware of the surrounding air traffic risks with up to 15 minutes in advance, without the help of air traffic controllers on the ground. This antecedence is much greater than the one provided by the current Traffic Collision Avoidance System (TCAS). ASAS uses a more advanced communication technology than Mode-C transponder, broadly used in the current civil aviation for collision avoidance purposes. The development of ASAS is being carried out intensively in Eurocontrol and in other initiatives in the United States of America, and this novel system is intended to work in parallel with the current collision avoidance systems, acting as safety nets. The present study approaches the ASAS application to improve the precision of spacing between aircraft that sequentially arrive at an airport, using the so called mathematical formalism \"Stochastically and Dynamically Coloured Petri Net\", for evaluating quantitative data about accident risk. These data indicate that the accident risk is significantly smaller when aircraft pairs use ASAS Spacing than when aircraft pairs do not use ASAS Spacing