Pub Date : 2023-04-18DOI: 10.1109/ICNS58246.2023.10124323
Ashim Kumar Thapa, J. Shortle, L. Sherry
Collision Risk Models (CRM) are used by regulatory safety agencies to determine the safe separation minima and monitor the air-to-air collision risk level of an airspace. CRMs estimate the expected number of aircraft collisions and "total" risk for a given Air Traffic concept-of-operation (e.g. parallel approaches). The fidelity of the models, and assumptions used in the models, are determined by the required confidence interval required for the safety analysis, the capabilities of current analytical and simulation methods, availability of empirical data sets, and the capabilities of computational resources.This paper provides an overview of the state-of-the-art for CRMs for Terminal Area operations. Opportunities to apply recently developed AI/ML, and data analytics methods such as analytical and rare-event simulation methods, availability of empirical data sets, and leverage available computational resources are identified.
{"title":"Air-to-Air Collision Risk Models (CRM) in the Terminal Airspace","authors":"Ashim Kumar Thapa, J. Shortle, L. Sherry","doi":"10.1109/ICNS58246.2023.10124323","DOIUrl":"https://doi.org/10.1109/ICNS58246.2023.10124323","url":null,"abstract":"Collision Risk Models (CRM) are used by regulatory safety agencies to determine the safe separation minima and monitor the air-to-air collision risk level of an airspace. CRMs estimate the expected number of aircraft collisions and \"total\" risk for a given Air Traffic concept-of-operation (e.g. parallel approaches). The fidelity of the models, and assumptions used in the models, are determined by the required confidence interval required for the safety analysis, the capabilities of current analytical and simulation methods, availability of empirical data sets, and the capabilities of computational resources.This paper provides an overview of the state-of-the-art for CRMs for Terminal Area operations. Opportunities to apply recently developed AI/ML, and data analytics methods such as analytical and rare-event simulation methods, availability of empirical data sets, and leverage available computational resources are identified.","PeriodicalId":103699,"journal":{"name":"2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)","volume":"284 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116096578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1109/ICNS58246.2023.10124301
Timothy Bleakley, E. Sunil
The Royal Netherlands Aerospace Center (NLR), in partnership with General Atomics Aeronautical Systems, Inc. (GA-ASI) and Information Systems Delft (ISD), has conducted several series of human-in-the-loop simulation experiments to assess and refine the safety and efficiency of fully integrating operations of large uncrewed aircraft systems (UAS) into typical civil air traffic scenarios. These experiments used a high-fidelity Air Traffic Control (ATC) simulation facility to provide professional controllers and pilots with the experience of introducing large UAS operations into otherwise familiar air traffic situations. Currently, there are no UAS operating approvals that would allow such tests to be conducted in the real world, so the experience gained and lessons learned are invaluable in preparing for safe and smooth introduction of large UAS into civil airspace operations in the near future.Detect and Avoid (DAA) technologies are key to allowing nonsegregated, beyond visual line-of-sight (BVLOS) operation of large UAS, by enabling their remote pilots to keep the universal right of way rules of the air, without the conventional ability to see out of the aircraft's cockpit. The focus of these experiments, therefore, has been to test DAA capabilities and operating procedures needed for remote pilots and air traffic controllers to maintain separation of the uncrewed aircraft (UA) from other aircraft and to avoid collisions. Scenarios were carefully designed to trigger DAA alerting and guidance to the remote pilot, requiring a response with appropriate procedures, including coordination with ATC, to assess the safety and operational efficiency of those procedures. Many of the scenarios required traffic to make procedural mistakes in order to create conflict geometries that would trigger DAA alerts. UAS contingencies were also incorporated, such as loss of C2 link, to evaluate remote pilot and controller response procedures.GA-ASI's SkyGuardian, a turboprop-powered, large fixed-wing UAS, was used as the performance model for a UAS operating from conventional runways that could perform flights as diverse as infrastructure surveying to cargo transport. Rotterdam airport and its surrounding airspace was selected as the operating context, to typify moderately busy and complex European airspace. The UAS flight scenarios spanned all the typical domestic airspace ATC roles, involving Tower, Approach and Route controllers, and included typical background commercial and general aviation traffic patterns and densities.The DAA capabilities tested are based on RTCA DO-365B Minimum Operational Performance Standards (MOPS). Earlier series of experiments tested the capabilities of a Class 1 system with air-to-air radar, active surveillance, ADS-B In and DAA alerting and guidance for en-route self-separation, plus Class 5 for DAA alerting and guidance in the terminal area. The latest series of experiments upgraded the DAA system to Class 2 capabilities with the addi
{"title":"Testing Operating Procedures for Large UAS with Detect and Avoid Capabilities in Civil Air Traffic Management Environments","authors":"Timothy Bleakley, E. Sunil","doi":"10.1109/ICNS58246.2023.10124301","DOIUrl":"https://doi.org/10.1109/ICNS58246.2023.10124301","url":null,"abstract":"The Royal Netherlands Aerospace Center (NLR), in partnership with General Atomics Aeronautical Systems, Inc. (GA-ASI) and Information Systems Delft (ISD), has conducted several series of human-in-the-loop simulation experiments to assess and refine the safety and efficiency of fully integrating operations of large uncrewed aircraft systems (UAS) into typical civil air traffic scenarios. These experiments used a high-fidelity Air Traffic Control (ATC) simulation facility to provide professional controllers and pilots with the experience of introducing large UAS operations into otherwise familiar air traffic situations. Currently, there are no UAS operating approvals that would allow such tests to be conducted in the real world, so the experience gained and lessons learned are invaluable in preparing for safe and smooth introduction of large UAS into civil airspace operations in the near future.Detect and Avoid (DAA) technologies are key to allowing nonsegregated, beyond visual line-of-sight (BVLOS) operation of large UAS, by enabling their remote pilots to keep the universal right of way rules of the air, without the conventional ability to see out of the aircraft's cockpit. The focus of these experiments, therefore, has been to test DAA capabilities and operating procedures needed for remote pilots and air traffic controllers to maintain separation of the uncrewed aircraft (UA) from other aircraft and to avoid collisions. Scenarios were carefully designed to trigger DAA alerting and guidance to the remote pilot, requiring a response with appropriate procedures, including coordination with ATC, to assess the safety and operational efficiency of those procedures. Many of the scenarios required traffic to make procedural mistakes in order to create conflict geometries that would trigger DAA alerts. UAS contingencies were also incorporated, such as loss of C2 link, to evaluate remote pilot and controller response procedures.GA-ASI's SkyGuardian, a turboprop-powered, large fixed-wing UAS, was used as the performance model for a UAS operating from conventional runways that could perform flights as diverse as infrastructure surveying to cargo transport. Rotterdam airport and its surrounding airspace was selected as the operating context, to typify moderately busy and complex European airspace. The UAS flight scenarios spanned all the typical domestic airspace ATC roles, involving Tower, Approach and Route controllers, and included typical background commercial and general aviation traffic patterns and densities.The DAA capabilities tested are based on RTCA DO-365B Minimum Operational Performance Standards (MOPS). Earlier series of experiments tested the capabilities of a Class 1 system with air-to-air radar, active surveillance, ADS-B In and DAA alerting and guidance for en-route self-separation, plus Class 5 for DAA alerting and guidance in the terminal area. The latest series of experiments upgraded the DAA system to Class 2 capabilities with the addi","PeriodicalId":103699,"journal":{"name":"2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116332878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1109/ICNS58246.2023.10124310
Junjie Zhao, Christopher Conrad, Quentin Delezenne, Yan Xu, A. Tsourdos
The UK Future Flight Vision and Roadmap defines how aviation in the UK is envisioned to develop by 2030. As part of the Future Flight demonstration segment, project HADO (High-intensity Autonomous Drone Operations) will develop, test, and deploy fully automated Unmanned Aircraft System (UAS) operations at London Heathrow airport. The resource-demanding nature of real-world tests, however, suggests that developing and improving the reliability and efficiency of virtual environment-based testing methods is indispensable for the evolution of such operations. Nonetheless, developing a high-fidelity and real-time virtual environment that enables the safe, scalable, and sustainable development, verification, and validation of UAS operations remains a daunting task. Notably, the need to integrate physical and virtual elements with a high degree of correlation presents a significant challenge. Consequently, as part of the synthetic test environment work package within the HADO project, this paper proposes a Digital Twin (DT) system to enable mixed-reality tests in the context of autonomous UAS operations. This connects a physical world to its digital counterpart made up of five distinct layers and several digital elements to support enhanced mixed-reality functionality. The paper highlights how the static layers of the synthetic test environment are built, and presents a DT prototype that supports mixed-reality test capabilities. In particular, the ability to inject virtual obstacles into physical test environments is demonstrated, highlighting how the sharp boundaries between virtual environments and reality can be blurred for safe, flexible, efficient, and effective testing of UAS operations.
{"title":"A Digital Twin Mixed-reality System for Testing Future Advanced Air Mobility Concepts: A Prototype","authors":"Junjie Zhao, Christopher Conrad, Quentin Delezenne, Yan Xu, A. Tsourdos","doi":"10.1109/ICNS58246.2023.10124310","DOIUrl":"https://doi.org/10.1109/ICNS58246.2023.10124310","url":null,"abstract":"The UK Future Flight Vision and Roadmap defines how aviation in the UK is envisioned to develop by 2030. As part of the Future Flight demonstration segment, project HADO (High-intensity Autonomous Drone Operations) will develop, test, and deploy fully automated Unmanned Aircraft System (UAS) operations at London Heathrow airport. The resource-demanding nature of real-world tests, however, suggests that developing and improving the reliability and efficiency of virtual environment-based testing methods is indispensable for the evolution of such operations. Nonetheless, developing a high-fidelity and real-time virtual environment that enables the safe, scalable, and sustainable development, verification, and validation of UAS operations remains a daunting task. Notably, the need to integrate physical and virtual elements with a high degree of correlation presents a significant challenge. Consequently, as part of the synthetic test environment work package within the HADO project, this paper proposes a Digital Twin (DT) system to enable mixed-reality tests in the context of autonomous UAS operations. This connects a physical world to its digital counterpart made up of five distinct layers and several digital elements to support enhanced mixed-reality functionality. The paper highlights how the static layers of the synthetic test environment are built, and presents a DT prototype that supports mixed-reality test capabilities. In particular, the ability to inject virtual obstacles into physical test environments is demonstrated, highlighting how the sharp boundaries between virtual environments and reality can be blurred for safe, flexible, efficient, and effective testing of UAS operations.","PeriodicalId":103699,"journal":{"name":"2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127893334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1109/ICNS58246.2023.10124260
A. Lacher, L. Ren, D. Maroney, Christopher Schulenberg, Jonathan Daniels
In this paper, we discuss an analysis method to describe the human and automation roles in increasingly autonomous aviation systems. Specifically, we discuss the relationship between humans and automation in the performance of individual functions in two dimensions, related to their responsibility and authority for the control loops associated with an individual intended function. This work builds upon concepts and terminology proposed by ASTM International in Autonomy Design and Operations in Aviation: Terminology and Requirements Framework (TR1) published in 2019 and is based in part on the on-going work of an ASTM Working Group (WK76044) under F39 Aircraft Systems. TR1 presented a Contextual Framework for analysis of automated aviation functions, and the working group is developing a practice for its implementation.The role of the human vs. the role of automation, which has been extensively discussed in the past, is fundamental to the implementation of the Contextual Framework. As such, it is worth a fresh look, particularly against the backdrop of the Contextual Framework. Our work has led to an analysis of the system state, which at any given time is defined by the role of the human and the role of automation in a two-dimensional space. We refer to this as Dimensional Role Analysis which uses a directed graph approach to clearly depict the nominal operating mode, the revisionary modes, and the triggering events that would cause transition. The analysis approach helps to establish clear delineations as to the responsibility and authority of humans and automation in the performance of individual intended functions. This work makes a clear distinction between authority, responsibility, and accountability consistent with the work of ASTM.Using the concepts and language presented in this paper could add clarity to discussions in the aviation community, as proponents seek to certify and obtain operational approval for increasingly autonomous systems in aviation. While this paper focuses on aircraft control, these approaches could also apply to other safety critical systems.
{"title":"Dimensional Role Analysis: The Role of Humans and Automation for Increasingly Autonomous Aviation Systems","authors":"A. Lacher, L. Ren, D. Maroney, Christopher Schulenberg, Jonathan Daniels","doi":"10.1109/ICNS58246.2023.10124260","DOIUrl":"https://doi.org/10.1109/ICNS58246.2023.10124260","url":null,"abstract":"In this paper, we discuss an analysis method to describe the human and automation roles in increasingly autonomous aviation systems. Specifically, we discuss the relationship between humans and automation in the performance of individual functions in two dimensions, related to their responsibility and authority for the control loops associated with an individual intended function. This work builds upon concepts and terminology proposed by ASTM International in Autonomy Design and Operations in Aviation: Terminology and Requirements Framework (TR1) published in 2019 and is based in part on the on-going work of an ASTM Working Group (WK76044) under F39 Aircraft Systems. TR1 presented a Contextual Framework for analysis of automated aviation functions, and the working group is developing a practice for its implementation.The role of the human vs. the role of automation, which has been extensively discussed in the past, is fundamental to the implementation of the Contextual Framework. As such, it is worth a fresh look, particularly against the backdrop of the Contextual Framework. Our work has led to an analysis of the system state, which at any given time is defined by the role of the human and the role of automation in a two-dimensional space. We refer to this as Dimensional Role Analysis which uses a directed graph approach to clearly depict the nominal operating mode, the revisionary modes, and the triggering events that would cause transition. The analysis approach helps to establish clear delineations as to the responsibility and authority of humans and automation in the performance of individual intended functions. This work makes a clear distinction between authority, responsibility, and accountability consistent with the work of ASTM.Using the concepts and language presented in this paper could add clarity to discussions in the aviation community, as proponents seek to certify and obtain operational approval for increasingly autonomous systems in aviation. While this paper focuses on aircraft control, these approaches could also apply to other safety critical systems.","PeriodicalId":103699,"journal":{"name":"2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124583660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1109/ICNS58246.2023.10124287
Phuoc H. Dang, M. A. Mohamed, S. Alam
Detection of air traffic conflicts in a weather constrained airspace is challenging given the inherent uncertainties and aircraft maneuvers which give rise to new conflict birth-points. Traditional conflict detection tools are untenable in such situations as they primarily rely on flight-plan, aircraft performance characteristics and trajectories projection in short-term (2-4 minutes). This work adopts a convolutional neural network (CNN) model, on radar-like images, for conflict detection task in a constrained airspace. The CNN models are well-known for their learning capabilities when dealing with unstructured data like pixelated images. In this study, historical ADS-B data with weather constrained airspace is input as pixelated images to the CNN model. The learned model was compared with two well-known models for conflict detection (CD). The results demonstrated that the CNN based model was able to predict off-nominal conflict with high accuracy. The CNN model also demonstrated its ability to predict off-nominal conflict early for a given ten-minute look-ahead window. The CNN based model also showed low levels of false alarm signals as compared to other models. Generally speaking, all models showed low probabilities of miss-detection, mostly in the early phase of the 10-minute look-ahead window. This novel approach may serve to develop effective CD algorithms with longer look-ahead time and may aid in early detection of air traffic conflicts in non-nominal scenarios.
{"title":"Image-based Conflict Detection with Convolutional Neural Network under Weather Uncertainty","authors":"Phuoc H. Dang, M. A. Mohamed, S. Alam","doi":"10.1109/ICNS58246.2023.10124287","DOIUrl":"https://doi.org/10.1109/ICNS58246.2023.10124287","url":null,"abstract":"Detection of air traffic conflicts in a weather constrained airspace is challenging given the inherent uncertainties and aircraft maneuvers which give rise to new conflict birth-points. Traditional conflict detection tools are untenable in such situations as they primarily rely on flight-plan, aircraft performance characteristics and trajectories projection in short-term (2-4 minutes). This work adopts a convolutional neural network (CNN) model, on radar-like images, for conflict detection task in a constrained airspace. The CNN models are well-known for their learning capabilities when dealing with unstructured data like pixelated images. In this study, historical ADS-B data with weather constrained airspace is input as pixelated images to the CNN model. The learned model was compared with two well-known models for conflict detection (CD). The results demonstrated that the CNN based model was able to predict off-nominal conflict with high accuracy. The CNN model also demonstrated its ability to predict off-nominal conflict early for a given ten-minute look-ahead window. The CNN based model also showed low levels of false alarm signals as compared to other models. Generally speaking, all models showed low probabilities of miss-detection, mostly in the early phase of the 10-minute look-ahead window. This novel approach may serve to develop effective CD algorithms with longer look-ahead time and may aid in early detection of air traffic conflicts in non-nominal scenarios.","PeriodicalId":103699,"journal":{"name":"2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130536397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1109/ICNS58246.2023.10124258
F. Wieland, D. Matolak, Zach Drescher
Achieving Advanced Air Mobility (AAM) on a scale envisioned by industry proponents and other stakeholders will require an Air-Ground Communication (AG Comm) system that is robust and resilient to failures. In this paper we describe a Machine Learning-based tool that quickly predicts communication path loss for AAM flights, a key metric for establishing and maintaining robust AG Comm. We have implemented this tool and tested it using both simulated scenarios and live flight data. This paper describes the tool itself and the results obtained comparing it with "ground truth" as established through physics-based ray-tracing computations.
{"title":"Quantifying AAM Communications Quality using Machine Learning","authors":"F. Wieland, D. Matolak, Zach Drescher","doi":"10.1109/ICNS58246.2023.10124258","DOIUrl":"https://doi.org/10.1109/ICNS58246.2023.10124258","url":null,"abstract":"Achieving Advanced Air Mobility (AAM) on a scale envisioned by industry proponents and other stakeholders will require an Air-Ground Communication (AG Comm) system that is robust and resilient to failures. In this paper we describe a Machine Learning-based tool that quickly predicts communication path loss for AAM flights, a key metric for establishing and maintaining robust AG Comm. We have implemented this tool and tested it using both simulated scenarios and live flight data. This paper describes the tool itself and the results obtained comparing it with \"ground truth\" as established through physics-based ray-tracing computations.","PeriodicalId":103699,"journal":{"name":"2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130966570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1109/ICNS58246.2023.10124296
Juul Vossen, Enrico Spinielli, Quinten Goens, R. Koelle
The European Air Transportation Network was significantly impacted by the COVID-19 pandemic, resulting in an unprecedented loss of flight connections. Utilizing a combination of graph representation learning and time series analysis, this paper studies the evolution of both the global connectivity as well as the structure of the European Air Transportation Network from January 2020 to December 2022. Specifically, it finds strong differences in recovery rates for flights across six different market segments. In terms of network structure, the study finds that structural roles that are present in the pre-covid network have seen a loss in performance over the course of the pandemic, but have recovered to pre-covid levels. Using regional changes in structural roles, this study identifies Italy as the region with the strongest increase and the United Kingdom as the region with the strongest decrease in structural role, finding substantial differences in recovery rates per market segment. Lastly, this study pays special attention on the effect of the Russia-Ukrainian war on the European Air Transportation Network.
{"title":"Studying the impact of COVID-19 on the European Air Transportation Network","authors":"Juul Vossen, Enrico Spinielli, Quinten Goens, R. Koelle","doi":"10.1109/ICNS58246.2023.10124296","DOIUrl":"https://doi.org/10.1109/ICNS58246.2023.10124296","url":null,"abstract":"The European Air Transportation Network was significantly impacted by the COVID-19 pandemic, resulting in an unprecedented loss of flight connections. Utilizing a combination of graph representation learning and time series analysis, this paper studies the evolution of both the global connectivity as well as the structure of the European Air Transportation Network from January 2020 to December 2022. Specifically, it finds strong differences in recovery rates for flights across six different market segments. In terms of network structure, the study finds that structural roles that are present in the pre-covid network have seen a loss in performance over the course of the pandemic, but have recovered to pre-covid levels. Using regional changes in structural roles, this study identifies Italy as the region with the strongest increase and the United Kingdom as the region with the strongest decrease in structural role, finding substantial differences in recovery rates per market segment. Lastly, this study pays special attention on the effect of the Russia-Ukrainian war on the European Air Transportation Network.","PeriodicalId":103699,"journal":{"name":"2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130450810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1109/ICNS58246.2023.10124325
Shelby S. Holdren, Max Z. Li, J. Hoffman
The U.S. National Airspace System (NAS), its interconnected operations, and resultant dynamics (e.g., flight delays, route configurations) can be understood at different scales. At the resolution of the entire NAS, a component (e.g., Denver Center) could be considered as one homogeneous node, with dependencies on other components (e.g., adjacent centers) and exogenous stakeholders (e.g., Air Traffic Control System Command Center, airline network operations centers). Understanding the connected behavior of the NAS in a data-driven and adaptive way is critical for rigorously determining whether interventions, strategic or tactical, were successful. However, within NAS components flight operations induce a multitude of relationships between parts of the airspace at many levels. To capture, analyze, and build upon such a connected and multi-scaled system, we require graph-based network models at varying resolutions, which can be adapted to fit a particular analysis use case. As an example, graphical representation at a higher resolution within a component may be required to capture nuanced behavior in analyses focused on local perturbations. In this work, we present a pipeline that constructs flexible graph-structured data from flight trajectories, and leverage this for different case studies within the NAS, all focused on evaluating different aspects of traffic flow management, and at different scales.
{"title":"Adaptable Graph Networks for Air Traffic Analysis Applications","authors":"Shelby S. Holdren, Max Z. Li, J. Hoffman","doi":"10.1109/ICNS58246.2023.10124325","DOIUrl":"https://doi.org/10.1109/ICNS58246.2023.10124325","url":null,"abstract":"The U.S. National Airspace System (NAS), its interconnected operations, and resultant dynamics (e.g., flight delays, route configurations) can be understood at different scales. At the resolution of the entire NAS, a component (e.g., Denver Center) could be considered as one homogeneous node, with dependencies on other components (e.g., adjacent centers) and exogenous stakeholders (e.g., Air Traffic Control System Command Center, airline network operations centers). Understanding the connected behavior of the NAS in a data-driven and adaptive way is critical for rigorously determining whether interventions, strategic or tactical, were successful. However, within NAS components flight operations induce a multitude of relationships between parts of the airspace at many levels. To capture, analyze, and build upon such a connected and multi-scaled system, we require graph-based network models at varying resolutions, which can be adapted to fit a particular analysis use case. As an example, graphical representation at a higher resolution within a component may be required to capture nuanced behavior in analyses focused on local perturbations. In this work, we present a pipeline that constructs flexible graph-structured data from flight trajectories, and leverage this for different case studies within the NAS, all focused on evaluating different aspects of traffic flow management, and at different scales.","PeriodicalId":103699,"journal":{"name":"2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125508627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1109/ICNS58246.2023.10124302
Thomas Ewert, Nils Mäurer
Faced with climate change and global warming, the reduction of greenhouse gas emissions, such as carbon dioxide, is a modern day focus of politics, industry and society. The aviation industry, responsible for about 3.5% of worldwide greenhouse gas emissions, is currently heavily depending on fossil fuels. As such, efforts are made to move towards a sustainable green aviation and carbon neutrality. Projects undertaken by authorities, airlines or manufacturers vary from new technologies like electric propulsion or Sustainable Aviation Fuel (SAF), to adjustments of operating procedures such as the retrieval of wake energy. [22] One already tested Wake Energy Retrieval (WER) technology is Airbus' fello'fly project.Hereby, different aircraft are positioned closely together on intercontinental flights, such that the succeeding aircraft can benefit from upward air movement within the wake created by the preceding one. Flight trials conducted in 2021 have shown a significant opportunity to reduce fuel consumption and therefore the carbon dioxide emissions and costs on long-range flights. However, to benefit from wake energy requires a reduction in currently established safety distances between aircraft. As introduction into service is envisioned as early as 2025, the fello'fly system will be based on already existing technologies.This paper focuses on possible safety and security implications created by this combination of reduced safety distances and reliance on legacy communication, navigation and surveillance technologies. First, regulations governing aircraft separation will be introduced, followed by an introduction to the concept of fello'fly. Furthermore, the paper offers an overview of the employed technologies and underscores their known cybersecurity vulnerabilities. The implications of using these systems alongside reduced separation in an evolving threat landscape are examined. The paper concludes by proposing necessary enhancements for a secure implementation of wake energy retrieval.
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