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Gestão Ótima do Fluxo de Tráfego Aéreo nas Zonas Terminais de Voo com Base em Redes Dinâmicas
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Abstract(s)
O aumento da procura sentido no setor do transporte aéreo refletiu-se no aumento da densidade do fluxo de aeronaves principalmente nas zonas terminais, sendo estas áreas designadas em inglês por Terminal Maneuvering Area (TMA) e fundamentais para a correta realização das aterragens e das descolagens. Esta sobrecarga foi gerida por inúmeros(as) agentes que observaram, controlaram e tomaram as decisões adequadas em cada momento, aliando essas tarefas com o(a) correto(a) tratamento e análise dos dados obtidos através de inúmeras fontes relevantes e dos programas computacionais que têm sido criados, desenvolvidos e implementados nas últimas décadas. Tendo por base esta problemática, pretendeu-se com o presente trabalho de investigação analisar, de forma concreta e crítica, a complexidade da otimização da gestão da capacidade das TMAs e propor medidas corretivas, obtendo uma estimativa preditiva do nível de ocupação das mesmas, em um determinado momento futuro, evitando a ocorrência de congestionamentos. O desejo de garantir e até mesmo de aumentar os níveis da segurança operacional motivaram a constante procura por métodos e metodologias, aliados(as) a programas computacionais. Por esse motivo, a recolha obtida através da revisão bibliográfica permitiu enunciar, descrever e exemplificar as principais medidas utilizadas até aos dias de hoje para gerir o fluxo de tráfego aéreo. Foi possível, dessa forma, evidenciar aspetos que poderão ser analisados, de forma mais detalhada, em trabalhos futuros, proporcionando sugestões de melhoria que beneficiem todos(as) os(as) utilizadores(as) deste setor. Desta forma, e através da modelação realizada com o suporte e a aplicação de redes dinâmicas, foi possível detetar o padrão de funcionamento do fluxo de tráfego aéreo na TMA do Aeroporto Humberto Delgado e sugerir as medidas adequadas que permitirão melhorar a gestão diária do fluxo de tráfego aéreo e que auxiliarão na previsão do mesmo e do nível de ocupação da TMA em análise, com o intuito de implementar essas medidas de forma atempada e segura. A modelação do fluxo de tráfego aéreo efetuada através da aplicação da Teoria das Cadeias de Markov permitiu a criação de um algoritmo cujo principal objetivo incidiu na otimização e na gestão entre a capacidade da TMA e a procura de aeronaves em cada momento, tendo em conta os inúmeros recursos existentes e podendo o mesmo ser extrapolado para outras TMAs. A metodologia selecionada representa um processo de otimização pois tem a capacidade de, através de um procedimento matemático, definir uma solução ótima para a gestão do nível de ocupação da TMA em estudo, maximizando o fluxo de tráfego aéreo com o intuito de satisfazer a procura registada, mas salvaguardando o nível de segurança operacional pretendido a qualquer momento e, simultaneamente, minimizando os atrasos verificados nas operações de tráfego aéreo. Esta otimização é conseguida através da análise da dinâmica verificada no fluxo de tráfego aéreo registado e da identificação de padrões de procura, sendo possível, dessa forma, analisar a variação do congestionamento do fluxo de tráfego aéreo ao longo do tempo. Salienta-se que esta abordagem nunca foi implementada no contexto do presente trabalho de investigação, revelando o seu carácter de originalidade. Através dos modelos criados foi possível identificar o grau de saturação da TMA selecionada e a necessidade de adaptação de medidas e/ou políticas já instauradas, tendo em conta as limitações físicas existentes e que restringem o aumento da procura do fluxo de tráfego aéreo. Desta forma verificou-se o elevado grau de constrangimento vivenciado, durante os períodos analisados, tendo um impacto direto na segurança das operações de tráfego aéreo. Assim sendo, é notória a necessidade da implementação de medidas que consigam minimizar os períodos de saturação, podendo ser tomadas pelas companhias de transporte aéreo através, por exemplo, da disponibilização de diferentes horários de planeamentos de voos e/ou rotas de voo alternativas que, de alguma forma, evitem as situações de constrangimento ou, por outro lado, de medidas tomadas pelas infraestruturas aeroportuárias e pelos(as) agentes que colaboram com o intuito de minimizarem os atrasos verificados nos diferentes voos e, dessa forma, evitando a aglomeração de aeronaves nas TMAs, salvaguardando e garantindo margem para lidarem com situações de emergência e/ou inesperadas. Por fim, mas não menos importante, salienta-se a importante capacidade de previsão obtida a partir dos modelos criados, permitindo dessa forma, e através do conhecimento do comportamento presente de uma determinada rede dinâmica, antever o seu comportamento num momento futuro e auxiliar na otimização da gestão do fluxo de tráfego aéreo. Esta característica é fundamental e de extrema utilidade não só para o conhecimento dos(as) agentes como também para os(as) estudiosos(as) empenhados(as) no desenvolvimento de programas computacionais que pretendem ser instrumentos indispensáveis neste processo.
The increase in demand felt in the air transport sector was reflected in the increase in the air traffic flow density, mainly in the Terminal Maneuvering Area (TMA), and essential for the correct performance of landings and take-offs. This overload was managed by numerous agents who observed, controlled and took the appropriate decisions at each moment, combining these tasks with the correct treatment and analysis of data obtained through numerous relevant sources and softwares that have been created, developed and implemented in recent decades. Based on this problem, this reseach work intended to analyze, in a concrete and critical way, the complexity of optimizing the TMAs capacity management, obtaining a predictive estimate of its occupancy level, at a certain future moment, avoiding the occurrence of congestion. The desire to guarantee and even increase the safety levels motivated the constant search for methods and methodologies, allied to softwares. For this reason, the collection obtained through bibliographic review allowed us to enunciate, describe and exemplify the main measures used until today to manage the air traffic flow. In this way, it was possible to highlight aspects that could be analyzed in more detail in future work, providing suggestions for improvements that benefit all users of this sector. In this way, and through the modeling carried out with the support and application of dynamic networks, it was possible to detect the operating pattern of the air traffic flow at the Humberto Delgado Airport TMA and suggest the appropriate measures that will improve the daily air traffic flow management and that will help in predicting the same and the occupation level of this TMA, with the aim of implementing these measures in a timely and safe manner. The modeling of the air traffic flow carried out through the application of the Markov Chain Theory allowed the creation of an algorithm whose main objective focused on the optimization and management between the TMAs capacity and the aircraft demand at each moment, considering the numerous existing resources and which can be extrapolated to other TMAs. The selected methodology represents an optimization process as it has the ability, through a mathematical procedure, to define an optimal solution for managing the TMA occupancy level under study, maximizing the air traffic flow to satisfy registered demand, but safeguarding the desired safety level at any time and, simultaneously, minimizing delays in air traffic operations. This optimization is achieved through the analysis of the dynamics observed in the recorded air traffic flow and the identification of demand patterns, making it possible to analyze the variation in air traffic flow congestion over time. It should be noted that this approach has never been implemented in the context of this research work, revealing its original nature. Through the models created, it was possible to identify the degree of saturation of the selected TMA and the need to adapt measures and/or policies already in place, considering the physical limitations that restrict the increase in demand for air traffic flow. In this way, the high degree of constraint experienced during the analyzed periods was verified, having a direct impact on the safety of air traffic operations. Therefore, there is a clear need to implement measures that can minimize saturation periods, which can be taken by airlines through, for example, the availability of different flight schedules and/or alternative flight routes that, somehow, avoid the observed constraints or, on the other hand, measures taken by the airports and by the agents that collaborate in order to minimize the delays verified in the different flights and, in this way, avoiding the agglomeration of aircraft in the TMAs, safeguarding and guaranteeing margin to deal with emergency and/or unexpected situations. Last but not least, the important predictive capacity obtained from the created models is highlighted, thus allowing, through knowledge of the present behavior of a given dynamic network, to predict its behavior in the future and help in the optimization of air traffic flow management. This characteristic is fundamental and extremely useful not only for the knowledge of agents but also for the scholars engaged in the development of softwares that intend to be indispensable instruments in this process.
The increase in demand felt in the air transport sector was reflected in the increase in the air traffic flow density, mainly in the Terminal Maneuvering Area (TMA), and essential for the correct performance of landings and take-offs. This overload was managed by numerous agents who observed, controlled and took the appropriate decisions at each moment, combining these tasks with the correct treatment and analysis of data obtained through numerous relevant sources and softwares that have been created, developed and implemented in recent decades. Based on this problem, this reseach work intended to analyze, in a concrete and critical way, the complexity of optimizing the TMAs capacity management, obtaining a predictive estimate of its occupancy level, at a certain future moment, avoiding the occurrence of congestion. The desire to guarantee and even increase the safety levels motivated the constant search for methods and methodologies, allied to softwares. For this reason, the collection obtained through bibliographic review allowed us to enunciate, describe and exemplify the main measures used until today to manage the air traffic flow. In this way, it was possible to highlight aspects that could be analyzed in more detail in future work, providing suggestions for improvements that benefit all users of this sector. In this way, and through the modeling carried out with the support and application of dynamic networks, it was possible to detect the operating pattern of the air traffic flow at the Humberto Delgado Airport TMA and suggest the appropriate measures that will improve the daily air traffic flow management and that will help in predicting the same and the occupation level of this TMA, with the aim of implementing these measures in a timely and safe manner. The modeling of the air traffic flow carried out through the application of the Markov Chain Theory allowed the creation of an algorithm whose main objective focused on the optimization and management between the TMAs capacity and the aircraft demand at each moment, considering the numerous existing resources and which can be extrapolated to other TMAs. The selected methodology represents an optimization process as it has the ability, through a mathematical procedure, to define an optimal solution for managing the TMA occupancy level under study, maximizing the air traffic flow to satisfy registered demand, but safeguarding the desired safety level at any time and, simultaneously, minimizing delays in air traffic operations. This optimization is achieved through the analysis of the dynamics observed in the recorded air traffic flow and the identification of demand patterns, making it possible to analyze the variation in air traffic flow congestion over time. It should be noted that this approach has never been implemented in the context of this research work, revealing its original nature. Through the models created, it was possible to identify the degree of saturation of the selected TMA and the need to adapt measures and/or policies already in place, considering the physical limitations that restrict the increase in demand for air traffic flow. In this way, the high degree of constraint experienced during the analyzed periods was verified, having a direct impact on the safety of air traffic operations. Therefore, there is a clear need to implement measures that can minimize saturation periods, which can be taken by airlines through, for example, the availability of different flight schedules and/or alternative flight routes that, somehow, avoid the observed constraints or, on the other hand, measures taken by the airports and by the agents that collaborate in order to minimize the delays verified in the different flights and, in this way, avoiding the agglomeration of aircraft in the TMAs, safeguarding and guaranteeing margin to deal with emergency and/or unexpected situations. Last but not least, the important predictive capacity obtained from the created models is highlighted, thus allowing, through knowledge of the present behavior of a given dynamic network, to predict its behavior in the future and help in the optimization of air traffic flow management. This characteristic is fundamental and extremely useful not only for the knowledge of agents but also for the scholars engaged in the development of softwares that intend to be indispensable instruments in this process.
Description
Keywords
Gestão do Tráfego Aéreo TMA Otimização do Fluxo Aéreo Redes Dinâmicas Cadeias de Markov Air Traffic Management