Advanced Air Traffic Management Research Articles

Development of an Intelligent Drone Management System for Integration into Smart City Transportation Networks

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditionally manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large number of drones expected to operate at low altitudes in complex urban environments. Therefore, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include the following: (i) developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; (ii) designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; (iii) improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and (iv) investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

New Generation Space Vehicle Operations in the United Arab Emirates – An Airspace and Risk Assessment

Air Transport is a highly technocentric and rapidly evolving industry. While certain legacy systems persist, more advanced technological concepts for carrying payloads and passengers further, faster, and more efficiently place this particular industry at the forefront of the most cutting-edge research and development initiatives in the fields of autonomous systems, artificial intelligence, cybersecurity, and advanced communication, navigation, surveillance applications. While drone technologies and applications have been widely and comprehensively addressed in recent years, the incipient hypersonic and suborbital commercial transport sector has received disproportionately less academic attention. Although certain suborbital passenger transport concepts such as the German Aerospace Center’s SpaceLiner are proposed for the more distant future, other concepts have reached operational status. A recent addition into the foray has been Blue Origin and its vertically launched New Shepherd autonomous vehicle. The United Arab Emirates, an increasingly active spacefaring nation, has signaled its interest. Among the nation’s highly ambitious short and long-term initiatives is the idea of launching space tourism flights from its own soil. Blue Origin, along with other candidates, has emerged as a contender to operate its New Shepard vehicle from a UAE-based spaceport. This paper explores how Next Generation Space Vehicle Operations (NGSVO) could impact a highly dense and compact airspace when operating from and to a UAE spaceport. Preliminary results suggest that under certain circumstances, NGSVOs may indeed affect existing Air Traffic Management (ATM) operations and pose increased safety risks while leading to higher fuel consumption and larger carbon dioxide footprints of regular air traffic. To establish a benchmark for future comparisons, the current study applies traditional airspace segregation methods to the UAE airspace to accommodate a specific type of NGSVO. Operational gains may be deduced by comparing the results of future studies applying more advanced ATM concepts to those reported in the current study.

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments.

The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors' knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99-100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.

Wind-optimal lateral trajectories for a multirotor aircraft in urban air mobility

The primary motivation for this paper is to quantify the operational benefits (energy consumption and flight duration) of flying wind-optimal lateral trajectories for short flights (less than 60 miles) anticipated in the urban environment. The optimal control model presented includes a wind model for quantifying the effect of wind on the lateral trajectory. The optimal control problem is numerically solved using the direct collocation method. Energy consumption and flight duration flying wind-optimal lateral trajectories are compared with corresponding values obtained flying great-circle paths between the same origin and destination pairs to determine the operational benefits of wind-optimal routing for short flights. The flight duration results for different scenarios are validated using a simulation tool designed and developed at NASA for exploring advanced air traffic management concepts. This research study suggests that for short flights in an urban environment, operational benefits of the wind-optimal lateral trajectories over the corresponding great-circle trajectories in terms of energy consumption and flight duration per flight are dependent on: i) wind field’s spatial variability, ii) wind magnitude, iii) the direction of route relative to the wind field, and iv) cruise segment length. The operational benefits observed in realistic flyable wind scenarios are less than 2.5%; these could be translated to an equivalent of a maximum of 2 min of cruise flight duration savings in the urban air mobility environment. As expected, headwinds and tailwinds along the flight route most significantly impact energy consumption and flight duration.

A Survey on Operation Concept, Advancements, and Challenging Issues of Urban Air Traffic Management

The recent growth and adoption of unmanned aerial vehicles (UAVs) is due to their low development cost, high aerial mobility, advanced battery technology, rotors, gyroscopes, GPS, cameras, sensors, and wide range of applications. The UAV offers new possibilities for business in civil and noncivil applications such as parcel delivery, aerial mapping, agriculture, wildlife conservation, surveillance, and search and rescue. However, considering the exponential growth of various types of aerial vehicles, it is clear that advanced air traffic management services are urgently needed to control and handle the ever-increasing air traffics. In this paper, we first present the two major low-altitude UAV traffic management systems in the United States and Europe and compare them thoroughly. We present the global vision and advancement of UAV traffic management system. To understand the complexity of future air transportation system, we discuss the necessary elements required for an urban air traffic management system. We provide key strategic elements for an advanced urban traffic management through connectivity, open source, safety enhancement, and automation to maximize its impacts on the innovative future air traffic and mobility system. In the end, we discuss the challenging security issues in urban traffic management system.

Estimating Operational Benefits of Air Traffic Management Harmonization in the Caribbean Oceanic Airspace

The Caribbean airspace is an operationally complex area because it consists of multiple regions operating under different air navigation service providers with distinct air traffic performance capabilities. This study evaluates the benefits of implementing the Space-Based Automatic Dependent Surveillance-Broadcast technology coupled with reduced and harmonized separation standards in the Caribbean airspace. The operational benefits of using advanced air traffic management procedures are investigated in terms of efficiency, capacity, and air traffic controllers’ taskload with computer simulations. Four scenarios are proposed in this paper, and flight operations with real-scale traffic volumes (low, medium, and high) were simulated. The results show that reducing separation minima in the Caribbean region without considering hemispherical rules provides average savings of 162 kg of fuel and 512 kg of carbon dioxide per flight. The estimated fuel saving could reach 180 million kg or 72 million dollars annually with the current aircraft fleet mix. This study demonstrates that employing advanced surveillance technologies increases the Caribbean airspace capacity and enables commercial flights to cruise at higher flight levels. Furthermore, the total number of tactical resolution maneuvers and the potential air traffic controllers’ taskload reduce in the proposed advanced air traffic scenarios.

Data-Driven Analysis of the Chaotic Characteristics of Air Traffic Flow

Understanding the chaos of air traffic flow is significant to the achievement of advanced air traffic management, and trajectory data are the basic material for studying the chaotic characteristics. However, at present, there are two main obstacles to this task, namely, large amounts of noise in the measured data and the tedium of existing data processing methods. This paper improves the incorrect trajectory processing method based on ADS-B trajectory data and proposes a method by which to quickly extract the traffic flow through a certain waypoint. Currently, the commonly used theoretical analysis tools for nonlinear complex systems include the classical nonlinear dynamics analysis method and the newly developed complex network-based analysis method. The latter is currently in an exploratory stage because it has just been introduced into the study of air traffic flow. From these two perspectives, the chaotic characteristics of air traffic flow are studied in the present work. From the perspective of nonlinear dynamics, the improved C-C method is used to calculate the reliability parameters, namely, the time delay τ and embedding dimension m, of phase-space reconstruction, and the maximum Lyapunov index is calculated by using the small data volume method to prove the existence of chaos in the system. From the perspective of complex networks, the construction of a visibility graph and horizontal visibility graph is used to prove the existence of chaos in the system, and the goodness-of-fit parameters of the degree distributions of two fitting methods under different time scales are evaluated, which provides support for the air traffic flow theory.

Exploring Dynamic Characteristics of Multi-State Air Traffic Flow: A Time Series Approach

Understanding the dynamics of air traffic flow is important to achieving advanced air traffic management. This work explores the dynamic evolution and fluctuation characteristics of multistate air traffic time series from a complex network perspective, which is essential for understanding the nature of an air traffic system. With the application of the fundamental diagram (FD), we discover that the relative velocity, flight distance and trajectory similarity are the three key variables for interpreting the arrival traffic flow states of the Xiamen Gaoqi International Airport. According to these three variables, time series are classified into four traffic states based on the K-means algorithm: free flow (FF), transitional flow (TF), slightly congested flow (SCF) and heavily congested flow (HCF). The extracted time series in different states are converted into complex networks using the visibility graph method. We analyze and compare the statistical features of the networks in the four states in terms of indexes, such as the degree distribution and network structure. The results indicate that the complex network characteristics can be used to distinguish air traffic states from the original traffic flow. Our work may be helpful for scholars and engineers to better understand the intrinsic nature of air traffic and for the development of intelligent assistant decision-making systems for air traffic management.

Estimating Approach Path Coverage of Aircraft-Derived Meteorological Data in Advanced Air Traffic Management Applications

If appropriately equipped, aircraft traveling throughout the global air space have the unique capability to measure and report high-resolution meteorological data, under all weather conditions. These data can have high economic value for both aviation and nonaviation uses by communicating a dense picture of weather conditions from thousands of ad hoc sensors operating daily in the global air space over areas of an aviation operational interest. However, ensuring that the technology provides adequate levels of service and safety requires a clear understanding of system performance characteristics in conjunction with sensor and data link capabilities, broadcast frequencies, equipage rates, and operational service environment requirements. This paper explores the density of coverage as a measure of performance by presenting the problem as a stochastic area coverage model of a homogeneous dynamic wireless ad hoc sensor network. A model was developed to relate the density needs of various advanced air traffic management applications to the spatial and temporal coverage of aircraft-sensed meteorological data in the airport terminal area. A comprehensive experimental design is presented with varying aircraft equipage rates, message broadcast frequencies, coverage granularities, and traffic demand levels. Infrastructure and sensor limitations are included to capture some realism within the model. Results are determined by solving this variation on a traditional set covering model using Monte Carlo simulation techniques.

The realistic consideration of human factors in model based simulation tools for the air traffic control domain

Advanced Air Traffic Management (ATM) concepts related to automation, airspace organization and operational procedures are driven by the overall goal to increase ATM system performance. Independently on the nature and/or impact of envisaged changes (e.g. from a short term procedure adjustment to a very long term operational concept or aid tools completion), the preliminary assessment of possible gains in airspace/airport capacity, safety and cost-effectiveness is done by running Model Based Simulations (MBSs, also known as Fast Time Simulations - FTS). Being a not human-in-the-loop technique, the reliability of a MBS results depend on the accuracy and significance of modeled human factors. Despite that, it can be observed in the practice that modeling tools commonly assume a generalized standardization of human behaviors and tasks and consider a very few range of work environment factors that, in the reality, affect the actual human-system performance. The present paper is aimed at opening a discussion about the possibility to keep task description and related weight at a high/general level, suitable for an efficient use of MBSs and, at the same time, increasing simulations reliability adopting some adjustment coming from the elaboration of further variables related to the human aspects of controllers workload.

Advanced air traffic management technologies: the ADS-B impact over ATM concepts. The case for Portugal

ADS-B is a very useful system to solve surveillance precision problems mainly if installation, operation and maintenance costs of alternative ones are too expensive when they are based on air traffic figures. This paper begins with some remarks about ADS-B technology, precisely to introduce the case study of Azores archipelago within Santa Maria FIR, in Portugal. On the basis of real scenarios of Pescara, Trabzon and Rhodes, and using EMOSIA model, a study is conducted to understand costs and return on investment on such equipment in Azores area. Finally, the paper concludes with some highlights of future research.

Air Traffic Controllers' Performance in Advance Air Traffic Management System: Part I—Performance Results

The current air traffic management (ATM) system will soon reach its capacity limits, so new concepts for the future are being developed. Many proposed schemes include shifting the task of maintaining safe distances between aircraft to ground-based automation and assigning air traffic controllers a supervisory role. This research investigated air traffic controller performance using advanced ATM concepts for the task of conflict resolution. Performance using a medium fidelity air traffic control simulator was investigated across 2 proportions of free-flying traffic and 3 types of decision support. Conflict resolution performance was significantly degraded when free-flying aircraft failed to negotiate a conflict and controllers had to intervene. Conflict resolution performance varied across the aiding condition and was superior when negotiation information was provided. However we did not find any differences in performance across proportions of free-flying traffic. Implications of this research are in further exploration of ATM concepts, design of decision aids, and training needs assessment.

ATOMS: Air Traffic Operations and Management Simulator

In this paper, we introduce the air traffic operations and management simulator (ATOMS), which is an air traffic and airspace modeling and simulation system for the analysis of free-flight concepts. This paper describes the design, architecture, functionality, and applications of the ATOMS. It is an intent-based simulator that discretizes the airspace in equal-sized hyper-rectangular cells to maintain intent reference points. It can simulate end-to-end airspace operations and air navigation procedures for conventional air traffic, as well as for free flight. Atmospheric and wind data that are modeled in the ATOMS result in accurate trajectory predictions. The ATOMS uses a multiagent-based modeling paradigm for modular design and easy integration of various air traffic subsystems. A variety of advanced air traffic management (ATM) concepts that are envisioned in free flight are prototyped in the ATOMS, including airborne separation assurance (ASA), cockpit display of traffic information (CDTI), weather avoidance, and decision support systems (DSSs). Experimental results indicate that advanced ATM concepts make a sound case for free flight; however, there is a need to investigate and understand their complex interaction under nonnominal scenarios.

Connectivity of Ad Hoc Networks for Advanced Air Traffic Management

This paper presents the concept and studies connectivities of wireless ad hoc networks among aircraft for enhanced situational awareness. Under this multi-hop broadcast concept, an aircraft would periodically broadcast not only its own state information, but also relay state information received from neighboring aircraft. In this paper, a basic architecture of such an airborne ad hoc network is established. The relationship between network connectivity and information reachability, which accounts for information latency and depends on transmission protocols, is discussed. Two general performance criteria are introduced that measure the connectivity performance of an airborne network subject to a specified maximum number of hops in the network. The first metric is the ratio of the total coverage area of a network cluster over that of a single aircraft. The second metric is the number of aircraft each individual aircraft can connect to within the network cluster. Three representative types of traffic scenarios are considered: a one-dimensional flight stream, two streams merging into one, and randomly distributed traffic over a horizontal region. In all cases, aircraft positions are checked against their conflict-free requirements. For the onedimensional traffic and merging traffic scenarios, the best, the worst, and the average values of the connectivity performance criteria are obtained via numerical simulations. For the two-dimensional random traffic scenario, random simulations are repeated, and both average connectivity performances and their standard deviations are calculated. Simulation results indicate that the connectivity of the proposed airborne ad hoc network is always better than that of the non-relay scheme. Overall, the proposed concept offers great flexibilities through the use of different transmission protocols and maximizes the benefits of a given digital data link.

A Formal Approach to the Analysis of Aircraft Protected Zone

The protected zone represents a region around a given aircraft that no other aircraft should penetrate for the safety of both aircraft, and defines the minimum separation requirements. In this paper, three major components of the protected zone and their interplays are identified: a vortex region, a safety buffer region, and a state-uncertainty region. A systematic procedure is devised for the analysis of the state-uncertainty region. In particular, models of trajectory controls are developed that can be used to represent different modes of pilot and/or autopilot controls, such as path feedback and non-path feedback. Composite protected zones under various conditions are estimated, and effective ways to reduce sizes of protected zones for advanced air traffic management are examined.

The role of advanced air traffic management in reducing the impact of aircraft noise and enabling aviation growth

Aviation is a critical component of the global economic infrastructure. If there is to be growth in aviation, however, the environmental impacts of aviation must be mitigated. While the benefits of advanced noise abatement procedures have been recognized, the primary obstacle to the implementation of these procedures remains the inability of air traffic controllers to maintain manually the precise sequencing and spacing required for maximum takeoff and landing rates in heavy traffic. Thus, introduction of automation that predicts the performance and noise impact of aircraft, and uses this information to assist the controller in determining and maintaining the appropriate sequencing and spacing, is critical to the successful utilization of noise abatement procedures.

Distributed Air/Ground Traffic Management for En Route Flight Operations

Distributed Air/Ground Traffic Management (DAG-TM) is a detailed instantiation of the Free Flight concept, formulated to serve as a foundation for NASA’s long-term research on advanced air traffic management concepts. Potentially revolutionary methods of air traffic management will be necessary to achieve the mature state of Free Flight. DAG-TM is a human-centered operational concept that seeks to minimize the impact of air traffic management constraints by leveraging new procedures and technological innovations in automation aids, information sharing, and supporting technologies. Under the DAG-TM paradigm of operations, flight crews, air traffic service providers (controllers), and aeronautical operational control personnel (flight planners/dispatchers) utilize distributed decision-making to maximize user flexibility and system throughput. This paper introduces the DAG-TM concept and provides a detailed description of the en route concept elements; the main purpose is to disseminate the DAG-TM concepts to the air traffic management community, and to motivate further exploration.

FACET: Future ATM Concepts Evaluation Tool

FACET (Future Air Traffic Management Concepts Evaluation Tool) is a simulation and analysis tool developed at the NASA Ames Research Center. This paper introduces the design, architecture, functionalities and applications of FACET. The purpose of FACET is to provide a simulation environment for exploration, development and evaluation of advanced Air Traffic Management concepts. FACET models system-wide airspace operations over the contiguous United States. The architecture of FACET strikes an appropriate balance between flexibility and fidelity, enabling it to model the trajectories of over 5,000 aircraft on a single desktop computer running on any of a wide variety of operating systems. FACET has proto-types of several advanced Air Traffic Management concepts: airborne self-separation; a decision support tool for direct routing; advanced traffic flow management techniques; and the integration of space launch vehicle operations into the U.S. National Airspace System.

The future functionalities of the flight management system

The availability of new Communication, Navigation and Surveillance technologies provides the basis to move towards advanced Air Traffic Management (ATM) concepts. Some of these concepts will impact the airborne Flight Management System (FMS) by introducing new functions with different flight time horizons. In this discussion, two main flight time horizon are defined: The tactical flight time horizon is between 30s and 10mn ahead of the aircraft current position. Airborne separation from traffic, terrain and adverse weather will be introduced in this time horizon as a tactical function. The strategic flight time horizon is more than 10mn ahead of the aircraft current position. Weather datafusion, enroute inflight replanning assistance and air-ground trajectory negotiation will be introduced in this time horizon as strategic functions. The organisation of the associated future flight management system can be synthesised in the scheme (figure 1). The data to be displayed to the pilot for his flight awareness are functions of the flight phase, the type of airspace and of the type of situation encountered. Globally these data can be shared as functions of their flight time horizons, and their topic, as it is described in the table below. In the cells are indicated the flight objects that these data address. SEXTANT as a major avionics manufacturer is leading research and development activities in the area of ATM related airborne flight management functions. Their existing or expected findings are described in this article.

Cognitive Performance for Multiple Operators in Complex Dynamic Airspace Systems: Computational Representation and Empirical Analyses

This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in enroute and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation through knowledge of the intention and action of remote operators. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.