Active Learning Metamodels for ATM Simulation Modeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables and their interrelationships, unknown stochastic phenomena, and ultimately human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their obvious advantages,simulation models can still end up being quite complex themselves. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves laborious and systematic analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values

Agent-based modeling and simulation for the design of the future european Air Traffic Management system: the experience of CASSIOPEIA

The SESAR (Single European Sky ATM Research) program is an ambitious re-search and development initiative to design the future European air traffic man-agement (ATM) system. The study of the behavior of ATM systems using agent-based modeling and simulation tools can help the development of new methods to improve their performance. This paper presents an overview of existing agent-based approaches in air transportation (paying special attention to the challenges that exist for the design of future ATM systems) and, subsequently, describes a new agent-based approach that we proposed in the CASSIOPEIA project, which was developed according to the goals of the SESAR program. In our approach, we use agent models for different ATM stakeholders, and, in contrast to previous work, our solution models new collaborative decision processes for flow traffic management, it uses an intermediate level of abstraction (useful for simulations at larger scales), and was designed to be a practical tool (open and reusable) for the development of different ATM studies. It was successfully applied in three stud-ies related to the design of future ATM systems in Europe

Active Learning for Air Traffic Management Simulation Metamodeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables, corresponding interrelationships, and the unpredictability of human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their clear advantages, these models can too suffer from high complexity and computational hindrances, especially when designed along with fine detail. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves exhausting and manual-driven intense analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate, via fast functions, the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values given a pre-specified input region

Agent-based performance assessment tool for general aviation operations under free flight

The objective of this research is to design and demonstrate an agent-based modeling and analysis tool for evaluating General Aviation (GA) pilot situation awareness under free flight air traffic management (ATM). A computational tool is developed to assess free flight's potential effect on GA operators, by combining an agent-based representation of the overall pilot/vehicle/ATM system with quantitative modelbased metrics of pilot SA. The model's performance is demonstrated in a set of simulation trials designed to measure the pilot agent's ability to recognize and correctly assess protected zone conflicts in free flight ATM, using information available from a hypothetical cockpit display of traffic information. A set of simulations is presented to examine the effect of sensor accuracy and attention allocation on pilot awareness of protected zone conflict hazards posed by intruder aircraft. The results show that reducing sensor accuracy leads to an increase in overall SA error, and that the pilot agent divides its attention over multiple traffic hazards in proportion to each intruder's hazard potential. This attention-sharing varies dynamically as the conflict situation changes, in a manner that is consistent with intuitive expectations

Future ATM Concepts Evaluation Tool (FACET) Interface Control Document

This Interface Control Document (ICD) documents the airspace adaptation and air traffic inputs of NASA's Future ATM Concepts and Evaluation Tool (FACET). Its intended audience is the project manager, project team, development team, and stakeholders interested in interfacing with the system. FACET equips Air Traffic Management (ATM) researchers and service providers with a way to explore, develop and evaluate advanced air transportation concepts before they are field-tested and eventually deployed. FACET is a flexible software tool that is capable of quickly generating and analyzing thousands of aircraft trajectories. It provides researchers with a simulation environment for preliminary testing of advanced ATM concepts. Using aircraft performance profiles, airspace models, weather data, and flight schedules, the tool models trajectories for the climb, cruise, and descent phases of flight for each type of aircraft. An advanced graphical interface displays traffic patterns in two and three dimensions, under various current and projected conditions for specific airspace regions or over the entire continental United States. The system is able to simulate a full day's dynamic national airspace system (NAS) operations, model system uncertainty, measure the impact of different decision-makers in the NAS, and provide analysis of the results in graphical form, including sector, airport, fix, and airway usage statistics. NASA researchers test and analyze the system-wide impact of new traffic flow management algorithms under anticipated air traffic growth projections on the nation's air traffic system. In addition to modeling the airspace system for NASA research, FACET has also successfully transitioned into a valuable tool for operational use. Federal Aviation Administration (FAA) traffic flow managers and commercial airline dispatchers have used FACET technology for real-time operations planning. FACET integrates live air traffic data from FAA radar systems and weather data from the National Weather Service to summarize NAS performance. This information allows system operators to reroute flights around congested airspace and severe weather to maintain safety and minimize delay. FACET also supports the planning and post-operational evaluation of reroute strategies at the national level to maximize system efficiency. For the commercial airline passenger, strategic planning with FACET can result in fewer flight delays and cancellations. The performance capabilities of FACET are largely due to its architecture, which strikes a balance between flexibility and fidelity. FACET is capable of modeling the airspace operations for the continental United States, processing thousands of aircraft on a single computer. FACET was written in Java and C, enabling the portability of its software to a variety of operating systems. In addition, FACET was designed with a modular software architecture to facilitate rapid prototyping of diverse ATM concepts. Several advanced ATM concepts have already been implemented in FACET, including aircraft self-separation, prediction of aircraft demand and sector congestion, system-wide impact assessment of traffic flow management constraints, and wind-optimal routing

Cyber Physical Security (CPS) Extension to Air Traffic Management (ATM) Testbed

The Air Traffic Management (ATM) Testbed is being developed at NASA to enable benefit, impact, safety and cost assessments for accelerating the deployment of Concept and Technologies (C&T) in the National Airspace System (NAS). Today, C&T introduction into the NAS takes decades. The primary reason for this is an inability to assess the operational impact of the interaction between the proposed C&T and operationally deployed systems (Realistic Technologies) in terms of NAS-wide safety, traffic flow efficiency, roles and workload of controllers and traffic managers, and impact on airline fleet operations. Transition of C&T to operations requires mathematical modeling and simulation, Human-in-the-Loop (HITL) testing and shadow-mode evaluation driven by operational data. Whereas interaction with the operational system during testing and stages of deployment is not permissible due to safety concerns, it is certainly possible to create a simulation environment that closely mimics the NAS using the same operational systems/hardware for enabling such assessments. This presentation focuses on a proposed Cyber Physical Security extension to the ATM Testbed for creating a modeling and simulation architecture to study how well the Air Traffic Management system will perform and analyze effectiveness of mitigating security measures against particular cyber-attack scenarios

Performance of voice and video conferencing over ATM and gigabit ethernet backbone networks

Gigabit Ethernet and ATM network technologies have been modeled as campus network backbones for the simulation-based comparison of their performance. Real-time voice and video conferencing traffic is used to compare the performance of both backbone technologies in terms of response times and packet end-to-end delays. Simulation results show that Gigabit Ethernet has been able to perform the same and in some cases better than ATM as a backbone network for video and voice conferencing providing network designers with a cheaper solution to meet the growing needs of bandwidth-hungry applications in a campus environment

Environmental impact analysis with the airspace concept evaluation system

The National Aeronautics and Space Administration (NASA) Ames Research Center has developed the Airspace Concept Evaluation System (ACES), which is a fast-time simulation tool for evaluating Air Traffic Management (ATM) systems. This paper describes linking a capability to ACES which can analyze the environmental impact of proposed future ATM systems. This provides the ability to quickly evaluate metrics associated with environmental impacts of aviation for inclusion in multi-dimensional cost-benefit analysis of concepts for evolution of the National Airspace System (NAS) over the next several decades. The methodology used here may be summarized as follows: 1) Standard Federal Aviation Administration (FAA) noise and emissions-inventory models, the Noise Impact Routing System (NIRS) and the Emissions and Dispersion Modeling System (EDMS), respectively, are linked to ACES simulation outputs; 2) appropriate modifications are made to ACES outputs to incorporate all information needed by the environmental models (e.g., specific airframe and engine data); 3) noise and emissions calculations are performed for all traffic and airports in the study area for each of several scenarios, as simulated by ACES; and 4) impacts of future scenarios are compared to the current NAS baseline scenario. This paper also provides the results of initial end-to-end, proof-of-concept runs of the integrated ACES and environmental-modeling capability. These preliminary results demonstrate that if no growth is likely to be impeded by significant environmental impacts that could negatively affect communities throughout the nation

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