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Advisor(s)
Abstract(s)
No decorrer destes últimos anos tem havido um enorme aumento na procura de
serviços de aviação, levando a que alguns aeroportos atinjam um determinado nível de
saturação. Tanto o setor de controlo de tráfego aéreo, como a infraestrutura
aeroportuária enfrentam limitações de capacidade. Contudo, o principal problema está
relacionado com a gestão inadequada dos recursos disponíveis, especialmente no que diz
respeito ao espaço aéreo.
Esta dissertação foca-se no desenvolvimento e avaliação de um modelo de
previsão de fluxos aéreos nas Áreas Terminais de Voo (Terminal Manoeuvring Areas -
TMA’s) utilizando o conceito de cadeias de Markov. As TMA’s são espaços aéreos
circundantes de um aeroporto, altamente congestionados e complexos. A previsão
precisa dos fluxos de tráfego nestas zonas terminais é essencial para garantir a segurança
e eficiência do sistema de controlo de tráfego aéreo. Como se tratam de zonas de
convergência, é necessário existir um limite de capacidade dependendo do aeroporto e,
por essa razão, é fundamental a previsão do fluxo, permitindo que o serviço de controlo
aéreo da TMA possa elaborar as estratégias táticas de modo a impedir o
congestionamento.
Nesta dissertação, propõe-se o uso de cadeias de Markov como uma abordagem
para modelar e prever os fluxos aéreos nas TMA’s. As cadeias de Markov são processos
estocásticos que descrevem a evolução de um sistema de estados discretos, onde a
probabilidade de transição entre estados depende somente do estado atual. Esta
propriedade torna as cadeias de Markov adequadas para modelar sistemas com
comportamento dinâmico e aleatório, como o caso do tráfego aéreo. Para desenvolver o
modelo de previsão, utilizaram-se dados históricos de tráfego aéreo nas zonas terminais
de Lisboa, de modo a construir uma matriz de transição de estados, que descreve as
probabilidades de transição entre os estados do sistema, que representam as diferentes
configurações de fluxos de tráfego aéreo nas TMA’s.
Os resultados obtidos sugerem que o modelo de previsão desenvolvido baseado
nas cadeias de Markov é eficaz na previsão de fluxos aéreos nas TMA’s. Foram efetuadas
tanto a previsão do número de chegadas de aeronaves em intervalos de uma hora como
a previsão em observações de apenas meia hora. Posto isto, foi realizada a análise do
estado permanente da rede, foi efetuada a previsão do próximo estado em apenas um
passo e posteriormente foi feita a previsão em ??-passos. Os resultados possibilitaram
identificar o estado mais provável, o estado considerado mais crítico. Desta forma, foi
possível analisar as consequências que os resultados podem trazer à administração do
aeroporto, resultando ou não no planeamento do aumento da sua capacidade de modo a
evitar a possibilidade de saturação.
Por fim, esta dissertação demonstra que as cadeias de Markov constituem uma
abordagem viável para a previsão de fluxos aéreos nas TMA’s. O modelo proposto
demonstra potencial para ser aplicado em sistemas reais de controlo de tráfego aéreo,
oferecendo benefícios significativos para a indústria da aviação em termos de eficiência
operacional e segurança. No entanto, são necessárias pesquisas e desenvolvimentos
adicionais para aperfeiçoar e adaptar o modelo em outras áreas terminais, em diferentes
aeroportos.
Over the past few years there has been a huge increase in demand for aviation services, leading to some airports reaching a certain level of saturation. Both the air traffic control industry and the airport infrastructure are facing capacity constraints. However, the main problem is related to the inadequate management of available resources, especially with regard to airspace. This dissertation focuses on the development and evaluation of a model to predict airflows in Terminal Manoeuvring Areas (TMA's) using the concept of Markov chains. TMA's are airspaces surrounding an airport that are highly congested and complex. Accurate prediction of traffic flows in these terminal areas is essential to ensure the safety and efficiency of the air traffic control system. As these are convergence zones, there needs to be a capacity limit depending on the airport, and for this reason flow forecasting is essential, allowing the TMA air traffic control service to devise tactical strategies in order to prevent congestion. In this dissertation, the use of Markov chains is proposed as an approach to model and predict air flows in TMA's. Markov chains are stochastic processes that describe the evolution of a system of discrete states, where the probability of transition between states depends only on the current state. This property makes Markov chains suitable for modeling systems with dynamic and random behavior, such as air traffic. To develop the prediction model, historical air traffic data was used in Lisbon's terminal zones, in order to build a state transition matrix, which describes the transition probabilities between states of the system, which represent the different configurations of air traffic flows in the TMA's. The results obtained suggest that the developed prediction model based on Markov chains is effective in predicting airflows in TMA's. Both the prediction of the number of aircraft arrivals in one-hour intervals and the prediction in half-hour observations were performed. Then, the permanent state of the network was analyzed, the next state was predicted in a single step, and then the next state was predicted in n steps. The results made it possible to identify the most likely state, the state considered most critical. In this way, it was possible to analyze the consequences that the results may bring to the airport management, resulting or not in planning to increase its capacity in order to avoid the possibility of saturation. Finally, this dissertation demonstrates that Markov chains constitute a viable approach for predicting airflows in TMA's. The proposed model shows potential to be applied in real air traffic control systems, offering significant benefits to the aviation industry in terms of operational efficiency and safety. However, further research and development is needed to refine and adapt the model in other terminal areas at different airports.
Over the past few years there has been a huge increase in demand for aviation services, leading to some airports reaching a certain level of saturation. Both the air traffic control industry and the airport infrastructure are facing capacity constraints. However, the main problem is related to the inadequate management of available resources, especially with regard to airspace. This dissertation focuses on the development and evaluation of a model to predict airflows in Terminal Manoeuvring Areas (TMA's) using the concept of Markov chains. TMA's are airspaces surrounding an airport that are highly congested and complex. Accurate prediction of traffic flows in these terminal areas is essential to ensure the safety and efficiency of the air traffic control system. As these are convergence zones, there needs to be a capacity limit depending on the airport, and for this reason flow forecasting is essential, allowing the TMA air traffic control service to devise tactical strategies in order to prevent congestion. In this dissertation, the use of Markov chains is proposed as an approach to model and predict air flows in TMA's. Markov chains are stochastic processes that describe the evolution of a system of discrete states, where the probability of transition between states depends only on the current state. This property makes Markov chains suitable for modeling systems with dynamic and random behavior, such as air traffic. To develop the prediction model, historical air traffic data was used in Lisbon's terminal zones, in order to build a state transition matrix, which describes the transition probabilities between states of the system, which represent the different configurations of air traffic flows in the TMA's. The results obtained suggest that the developed prediction model based on Markov chains is effective in predicting airflows in TMA's. Both the prediction of the number of aircraft arrivals in one-hour intervals and the prediction in half-hour observations were performed. Then, the permanent state of the network was analyzed, the next state was predicted in a single step, and then the next state was predicted in n steps. The results made it possible to identify the most likely state, the state considered most critical. In this way, it was possible to analyze the consequences that the results may bring to the airport management, resulting or not in planning to increase its capacity in order to avoid the possibility of saturation. Finally, this dissertation demonstrates that Markov chains constitute a viable approach for predicting airflows in TMA's. The proposed model shows potential to be applied in real air traffic control systems, offering significant benefits to the aviation industry in terms of operational efficiency and safety. However, further research and development is needed to refine and adapt the model in other terminal areas at different airports.
Description
Keywords
Área Terminal de Voo Cadeias de Markov Capacidade do Espaço Aéreo Previsão de Fluxo Tráfego Aéreo