Existing and Required Modeling Capabilities for Evaluating ATM Systems and Concepts

ATM systems throughout the world are entering a period of major transition and change. The combination of important technological developments and of the globalization of the air transportation industry has necessitated a reexamination of some of the fundamental premises of existing Air Traffic Management (ATM) concepts. New ATM concepts have to be examined, concepts that may place more emphasis on: strategic traffic management; planning and control; partial decentralization of decision-making; and added reliance on the aircraft to carry out strategic ATM plans, with ground controllers confined primarily to a monitoring and supervisory role. 'Free Flight' is a case in point. In order to study, evaluate and validate such new concepts, the ATM community will have to rely heavily on models and computer-based tools/utilities, covering a wide range of issues and metrics related to safety, capacity and efficiency. The state of the art in such modeling support is adequate in some respects, but clearly deficient in others. It is the objective of this study to assist in: (1) assessing the strengths and weaknesses of existing fast-time models and tools for the study of ATM systems and concepts and (2) identifying and prioritizing the requirements for the development of additional modeling capabilities in the near future. A three-stage process has been followed to this purpose: 1. Through the analysis of two case studies involving future ATM system scenarios, as well as through expert assessment, modeling capabilities and supporting tools needed for testing and validating future ATM systems and concepts were identified and described. 2. Existing fast-time ATM models and support tools were reviewed and assessed with regard to the degree to which they offer the capabilities identified under Step 1. 3 . The findings of 1 and 2 were combined to draw conclusions about (1) the best capabilities currently existing, (2) the types of concept testing and validation that can be carried out reliably with such existing capabilities and (3) the currently unavailable modeling capabilities that should receive high priority for near-term research and development. It should be emphasized that the study is concerned only with the class of 'fast time' analytical and simulation models. 'Real time' models, that typically involve humans-in-the-loop, comprise another extensive class which is not addressed in this report. However, the relationship between some of the fast-time models reviewed and a few well-known real-time models is identified in several parts of this report and the potential benefits from the combined use of these two classes of models-a very important subject-are discussed in chapters 4 and 7

An Airspace Simulator for Separation Management Research

Air Traffic Management (ATM) systems are undergoing a period of major transformation and modernisation, requiring and enabling new separation management (SM) methods. Many novel SM functions, roles and concepts are being explored using ATM simulators. Commercial simulators are capable, high-fidelity tools, but tend to be complex and inaccessible. The Airspace Simulator is a fast-time, discrete event simulator originally designed for exploratory ATM research. This thesis describes the redevelopment of the Airspace Simulator into a simulation platform better suited for researching and evaluating SM in future airspace. The Airspace Simulator-II has the advantage of new functionality and greater fidelity, while remaining high-speed, accessible and readily adaptable. The simulator models FMS-like spherical earth navigation and autopilot flight control with an average cross track error of 0.05 nmi for waypoint-defined routes in variable wind-fields. Trajectories are computed using the BADA v3.8 tabulated database to model the performance of 318 aircraft types. The simulator was demonstrated with up to 4000 total aircraft, and trajectories for 300 simultaneous aircraft were computed over 900 times faster than real-time. Datalink and radio-telephony communications are modelled between the air traffic and ATM systems. Surveillance is provided through ADS-B-like broadcasts, and an algorithm was developed to automatically merge instructions from conflict resolution systems with existing flight plans. Alternate communication, navigation, and separation modes were designed to permit the study of mixed-mode operations. Errors due to wind, navigational wander, communication latencies, and localised information states are modelled to facilitate research into the robustness of SM systems. The simulator incorporates a traffic visualisation tool and was networked to conflict detection and resolution software through a TCP/IP connection. A scenario generator was designed to automatically prepare flight plans for a large variety of two-aircraft encounters to support stochastic SM experiments. The simulator, scenario generator, and resolver were used for the preliminary analysis of a novel concept for automated SM over radio-telephony using progressive track angle vectoring

A teamwork-oriented air traffic control simulator

Air Traffic Control (ATC) is a complicated domain in which many specialists should collaborate and communicate with each other in order to guarantee safe and efficient air traffic. A significant number of air traffic control errors are associated with either faulty coordination between ATC actors, or a failure of some kind of team coordination. These errors are likely to increase in the future as aircraft density increases. Many researchers suggest that the introduction of team and teamwork concepts during the training phase of the ATC actors will be in help to reduce the amount of these errors. The objective of this research is to conceive, design, and implement a teamwork-oriented Air Traffic Control simulator that can be easily installed and used in ATC schools. The product of this thesis will be a complete software package that allows trainees in the different ATC specialties to work together in the same manner as they do "on-the-job" in order to collaboratively manage an air traffic situation. This type of simulator should allow air traffic control trainees to acquire more robust coordination skills and reduce the amount of traffic control errors caused by lack of teamwork in actual ATC training situations.http://archive.org/details/ateamworkoriente109452716Tunisian Air Force author.Approved for public release; distribution is unlimited

A Hybrid Tabu/Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling

As air traffic continues to increase, air traffic flow management is becoming more challenging to effectively and efficiently utilize airport capacity without compromising safety, environmental and economic requirements. Since runways are often the primary limiting factor in airport capacity, runway operations scheduling emerge as an important problem to be solved to alleviate flight delays and air traffic congestion while reducing unnecessary fuel consumption and negative environmental impacts. However, even a moderately sized real-life runway operations scheduling problem tends to be too complex to be solved by analytical methods, where all mathematical models for this problem belong to the complexity class of NP-Hard in a strong sense due to combinatorial nature of the problem. Therefore, it is only possible to solve practical runway operations scheduling problem by making a large number of simplifications and assumptions in a deterministic context. As a result, most analytical models proposed in the literature suffer from too much abstraction, avoid uncertainties and, in turn, have little applicability in practice. On the other hand, simulation-based methods have the capability to characterize complex and stochastic real-life runway operations in detail, and to cope with several constraints and stakeholders’ preferences, which are commonly considered as important factors in practice. This dissertation proposes a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling problem. The SbO approach utilizes a discrete-event simulation model for accounting for uncertain conditions, and an optimization component for finding the best known Pareto set of solutions. This approach explicitly considers uncertainty to decrease the real operational cost of the runway operations as well as fairness among aircraft as part of the optimization process. Due to the problem’s large, complex and unstructured search space, a hybrid Tabu/Scatter Search algorithm is developed to find solutions by using an elitist strategy to preserve non-dominated solutions, a dynamic update mechanism to produce high-quality solutions and a rebuilding strategy to promote solution diversity. The proposed algorithm is applied to bi-objective (i.e., maximizing runway utilization and fairness) runway operations schedule optimization as the optimization component of the SbO framework, where the developed simulation model acts as an external function evaluator. To the best of our knowledge, this is the first SbO approach that explicitly considers uncertainties in the development of schedules for runway operations as well as considers fairness as a secondary objective. In addition, computational experiments are conducted using real-life datasets for a major US airport to demonstrate that the proposed approach is effective and computationally tractable in a practical sense. In the experimental design, statistical design of experiments method is employed to analyze the impacts of parameters on the simulation as well as on the optimization component’s performance, and to identify the appropriate parameter levels. The results show that the implementation of the proposed SbO approach provides operational benefits when compared to First-Come-First-Served (FCFS) and deterministic approaches without compromising schedule fairness. It is also shown that proposed algorithm is capable of generating a set of solutions that represent the inherent trade-offs between the objectives that are considered. The proposed decision-making algorithm might be used as part of decision support tools to aid air traffic controllers in solving the real-life runway operations scheduling problem

A multi-level predictive methodology for terminal area air traffic flow

Over the past few decades, the air transportation system has grown significantly. In particular, the number of passengers using air transportation has greatly increased. As the demand for air travel expands, airport departure/arrival demand almost reaches its capacity. In consequence, the level of delays increases since the system capacity cannot manage the increased demand. With this trend, the national airspace system (NAS) will be saturated, and the congestion at the airport will become even more severe. As a result of congestion, a considerable number of flights experience delays. According to the Bureau of Transportation Statistics (BTS), over 1 million flights are operated in a year, and about twenty percent of all scheduled commercial flights are delayed more than 15 minutes. These delays cost billions of dollars annually for airlines, passengers, and the US economy. Therefore, this study seeks to find out why the delays occur and to analyze patterns in which the delays occurred. Analysis of airport operations generally falls into a macro or micro perspective. At the macro point of view, very few details are considered, and delays are aggregated at the airport level. Especially, shortfalls in airport capacity and a capacity-demand imbalance are the primary causes of delays in this respect. In the micro perspective, each aircraft is modeled individually, and the causes of delays are reproduced as precisely as possible. Micro reasons for air traffic delays include inclement weather, mechanics problems, operation issues. In this regard, this research proposes a methodology that can efficiently and practically predict macro and micro-level air traffic flow in the terminal area. For a macro-level analysis of delays, artificial neural networks models are proposed to predict the hourly airport capacity. Multi-layer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM) are trained with historical weather and airport capacity data of Hartsfield-Jackson Atlanta airport (ATL). In the performance evaluation, the models have presented decent predictive performance and successfully predicted the test data as well as the training data. On the other hand, Random Forests and AdaBoost are implemented in the micro-level modeling of the air traffic. The micro-level models trained with on-time flight performance data and corresponding weather data focus on a classification of the individual flight delays. The model provides interpretability and imbalanced data handling while the accuracy is as good as the existing methods. Lastly, the predictive model for individual flight delays is refined using the cost-proportionate rejection sampling (costing) method. Along with the integration of the costing method, general machine learning algorithms have been converted to cost-sensitive classifiers. The cost-sensitive classifiers were able to account for asymmetric misclassification costs without losing their diagnostic functionality as binary classifiers. This study presents a data-driven approach to air traffic flow management that can effectively utilize air traffic data accumulated over decades. Through data analysis from the macro and micro perspective, an integrated methodology for terminal air traffic flow prediction is provided. An accurate prediction of the airport capacity and individual flight delays will assist stakeholders in taking more informed decisions.Ph.D

Simulation-Based Evolutionary Optimization of Air Traffic Management

In the context of aerospace engineering, the optimization of processes may often require to solve multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities, possibly involving highly-expensive objective function evaluations. In Air Traffic Management (ATM), the optimization of procedures and protocols becomes even more complicated, due to the involve-ment of human controllers, which act as final decision points in the control chain. In this article, we propose the use of computational intelligence techniques, such as Agent-Based Modelling and Simulation (ABMS)and Evolutionary Computing (EC), to design a simulation-based distributed architecture to optimize control plans and procedures in the context of ATM. We rely on Agent-Based fast-time simulations to carry out offline what-if analysis of multiple scenarios, also taking into account human-related decisions, during the strategic or pre-tactical phases. The scenarios are constructed using real-world traffic data traces, while multiple optimization variables governed by an EC algorithm allow to explore the search space to identify the best solutions. Our optimization approach relies on ad-hoc multi-objective performance metrics which allow to assess the goodness of the control of aircraft and air traffic regulations. We present experimental results which prove the viability of our approach, comparing them with real-world data traces, and proving their meaningfulness from an Air Traffic Control perspective

On-line decision support for take-off runaway scheduling at London Heathrow Airport

The research problem considered in this thesis was presented by NATS, who are responsible for the take-off runway scheduling at London Heathrow airport. The sequence in which aircraft take off is very important and can have a huge effect upon the throughput of the runway and the consequent delay for aircraft awaiting take-off. Sequence-dependent separations apply between aircraft at take-off, some aircraft have time-slots within which they must take-off and all re-sequencing performed by the runway controller has to take place within restrictive areas of the airport surface called holding areas. Despite the complexity of the task and the short decision time available, take-off sequencing is performed manually by runway controllers. In such a rapidly changing environment, with much communication and observation demanded of the busy controller, it is hardly surprising that sub-optimal mental heuristics are currently used. The task presented by NATS was to develop the decision-making algorithms for a decision support tool to aid a runway controller to solve this complex real-world problem. A design for such a system is presented in this thesis. Although the decision support system presents only a take-off sequence to controllers, it is vitally important that the movement within the holding area that is required in order to achieve the re-sequencing is both easy to identify and acceptable to controllers. A key objective of the selected design is to ensure that this will always be the case. Both regulatory information and details of controller working methods and preferences were utilised to ensure that the presented sequences will not only be achievable but will also be acceptable to controllers. A simulation was developed to test the system and permit an evaluation of the potential benefits. Experiments showed that the decision support system found take-off sequences which significantly reduced the delay compared with those that the runway controllers actually used. These sequences had an equity of delay comparable with that in the sequences the controllers generated, and were achieved in a very similar way. Much of the benefit that was gained was a result of the decision support system having visibility of the taxiing aircraft in addition to those already queueing for the runway. The effects of uncertainty in taxi times and differing planning horizons are explicitly considered in this thesis. The limited decision time available ensures that it is not practical for a runway controller to consider as many aircraft as the decision support algorithms can. The results presented in this thesis indicate that huge benefits may be possible from the development of a system to simplify the sequencing task for the controllers while simultaneously giving them greater visibility of taxiing aircraft. Even beyond these benefits, however, the system described here will also be seen to have further potential benefits, such as for evaluating the effects of constraints upon the departure system or the flexibility of holding area structures

On-line decision support for take-off runaway scheduling at London Heathrow Airport

The research problem considered in this thesis was presented by NATS, who are responsible for the take-off runway scheduling at London Heathrow airport. The sequence in which aircraft take off is very important and can have a huge effect upon the throughput of the runway and the consequent delay for aircraft awaiting take-off. Sequence-dependent separations apply between aircraft at take-off, some aircraft have time-slots within which they must take-off and all re-sequencing performed by the runway controller has to take place within restrictive areas of the airport surface called holding areas. Despite the complexity of the task and the short decision time available, take-off sequencing is performed manually by runway controllers. In such a rapidly changing environment, with much communication and observation demanded of the busy controller, it is hardly surprising that sub-optimal mental heuristics are currently used. The task presented by NATS was to develop the decision-making algorithms for a decision support tool to aid a runway controller to solve this complex real-world problem. A design for such a system is presented in this thesis. Although the decision support system presents only a take-off sequence to controllers, it is vitally important that the movement within the holding area that is required in order to achieve the re-sequencing is both easy to identify and acceptable to controllers. A key objective of the selected design is to ensure that this will always be the case. Both regulatory information and details of controller working methods and preferences were utilised to ensure that the presented sequences will not only be achievable but will also be acceptable to controllers. A simulation was developed to test the system and permit an evaluation of the potential benefits. Experiments showed that the decision support system found take-off sequences which significantly reduced the delay compared with those that the runway controllers actually used. These sequences had an equity of delay comparable with that in the sequences the controllers generated, and were achieved in a very similar way. Much of the benefit that was gained was a result of the decision support system having visibility of the taxiing aircraft in addition to those already queueing for the runway. The effects of uncertainty in taxi times and differing planning horizons are explicitly considered in this thesis. The limited decision time available ensures that it is not practical for a runway controller to consider as many aircraft as the decision support algorithms can. The results presented in this thesis indicate that huge benefits may be possible from the development of a system to simplify the sequencing task for the controllers while simultaneously giving them greater visibility of taxiing aircraft. Even beyond these benefits, however, the system described here will also be seen to have further potential benefits, such as for evaluating the effects of constraints upon the departure system or the flexibility of holding area structures