1,133 research outputs found
Trajectory Clustering and an Application to Airspace Monitoring
This paper presents a framework aimed at monitoring the behavior of aircraft
in a given airspace. Nominal trajectories are determined and learned using data
driven methods. Standard procedures are used by air traffic controllers (ATC)
to guide aircraft, ensure the safety of the airspace, and to maximize the
runway occupancy. Even though standard procedures are used by ATC, the control
of the aircraft remains with the pilots, leading to a large variability in the
flight patterns observed. Two methods to identify typical operations and their
variability from recorded radar tracks are presented. This knowledge base is
then used to monitor the conformance of current operations against operations
previously identified as standard. A tool called AirTrajectoryMiner is
presented, aiming at monitoring the instantaneous health of the airspace, in
real time. The airspace is "healthy" when all aircraft are flying according to
the nominal procedures. A measure of complexity is introduced, measuring the
conformance of current flight to nominal flight patterns. When an aircraft does
not conform, the complexity increases as more attention from ATC is required to
ensure a safe separation between aircraft.Comment: 15 pages, 20 figure
Empirical exploration of air traffic and human dynamics in terminal airspaces
Air traffic is widely known as a complex, task-critical techno-social system,
with numerous interactions between airspace, procedures, aircraft and air
traffic controllers. In order to develop and deploy high-level operational
concepts and automation systems scientifically and effectively, it is essential
to conduct an in-depth investigation on the intrinsic traffic-human dynamics
and characteristics, which is not widely seen in the literature. To fill this
gap, we propose a multi-layer network to model and analyze air traffic systems.
A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN)
encapsulate critical physical and operational characteristics; an Integrated
Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network
(ICCN) are formulated to represent air traffic flow transmissions and
intervention from air traffic controllers, respectively. Furthermore, a set of
analytical metrics including network variables, complex network attributes,
controllers' cognitive complexity, and chaotic metrics are introduced and
applied in a case study of Guangzhou terminal airspace. Empirical results show
the existence of fundamental diagram and macroscopic fundamental diagram at the
route, sector and terminal levels. Moreover, the dynamics and underlying
mechanisms of "ATCOs-flow" interactions are revealed and interpreted by
adaptive meta-cognition strategies based on network analysis of the ICCN.
Finally, at the system level, chaos is identified in conflict system and human
behavioral system when traffic switch to the semi-stable or congested phase.
This study offers analytical tools for understanding the complex human-flow
interactions at potentially a broad range of air traffic systems, and underpins
future developments and automation of intelligent air traffic management
systems.Comment: 30 pages, 28 figures, currently under revie
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Visual analytics of flight trajectories for uncovering decision making strategies
In air traffic management and control, movement data describing actual and planned flights are used for planning, monitoring and post-operation analysis purposes with the goal of increased efficient utilization of air space capacities (in terms of delay reduction or flight efficiency), without compromising the safety of passengers and cargo, nor timeliness of flights. From flight data, it is possible to extract valuable information concerning preferences and decision making of airlines (e.g. route choice) and air traffic managers and controllers (e.g. flight rerouting or optimizing flight times), features whose understanding is intended as a key driver for bringing operational performance benefits. In this paper, we propose a suite of visual analytics techniques for supporting assessment of flight data quality and data analysis workflows centred on revealing decision making preferences
Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?
Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations
Identification of centroids of Mohammed V airport arrivals.
This paper presents a flight trajectory data analytics framework for identifying spatial and temporal patterns in aircraft movement and providing a high-fidelity characterization of air traffic flows. The framework includes three modules : Collecting Data, Resampling trajectories, and Clustering air traffic flows at temporal and spacial scale. Different machine learning techniques are especially incorporated into the three modules to process aircraft trajectory data and enable the characterization of traffic flows
Discussion on density-based clustering methods applied for automated identification of airspace flows
Air Traffic Management systems generate a huge amount of track data daily. Flight trajectories can be clustered to extract main air traffic flows by means of unsupervised machine learning techniques. A well-known methodology for unsupervised extraction of air traffic flows conducts a two-step process. The first step reduces the dimensionality of the track data, whereas the second step clusters the data based on a density-based algorithm, DBSCAN. This paper explores advancements in density-based clustering such as OPTICS or HDBSCAN*. This assessment is based on quantitative and qualitative evaluations of the clustering solutions offered by these algorithms. In addition, the paper proposes a hierarchical clustering algorithm for handling noise in this methodology. This algorithm is based on a recursive application of DBSCAN* (RDBSCAN*). The paper demonstrates the sensitivity of these algorithms to different hyper-parameters, recommending a specific setting for the main one, which is common for all methods. RDBSCAN* outperforms the other algorithms in terms of the density-based internal validity metric. Finally, the outcome of the clustering shows that the algorithm extracts main clusters of the dataset effectively, connecting outliers to these main clusters
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