Detecting and solving aircraft conflicts, which occur when aircraft sharing the same airspace are too close to each other according to their predicted trajectories, is a crucial problem in Air ...Traffic Management. We focus on mixed-integer optimization models based on speed regulation. We first solve the problem to global optimality by means of an exact solver. Since the problem is very difficult to solve, we also propose a heuristic procedure where the problem is decomposed and it is locally exactly solved. Computational results show that the proposed approach provides satisfactory results.
Both conflict resolution aid (CRA) and vertical situation display (VSD) systems may contribute to air traffic control (ATC) operations. However, their effectiveness still needs to be examined before ...being widely adopted in ATC facilities. This study aims to examine empirically the use of CRA and VSD as well as the systems’ interaction in ATC operations. It was found that CRA benefited conflict resolution performance by 13⋅7% and lowered workload by 46⋅4% compared with manually performing the task. The VSD could also reduce the air traffic controllers’ (ATCOs) workload and improve their situation awareness. Ultimately, when the first CRA failure occurred, the situation awareness supported by VSD offset the performance decrements by 30%. The findings from this study demonstrate that integrating VSD with CRA would benefit ATC operations, regardless of the CRA's imperfection.
While studies exist that compare different physiological variables with respect to their association with mental workload, it is still largely unclear which variables supply the best information ...about momentary workload of an individual and what is the benefit of combining them. We investigated workload using the n-back task, controlling for body movements and visual input. We recorded EEG, skin conductance, respiration, ECG, pupil size and eye blinks of 14 subjects. Various variables were extracted from these recordings and used as features in individually tuned classification models. Online classification was simulated by using the first part of the data as training set and the last part of the data for testing the models. The results indicate that EEG performs best, followed by eye related measures and peripheral physiology. Combining variables from different sensors did not significantly improve workload assessment over the best performing sensor alone. Best classification accuracy, a little over 90%, was reached for distinguishing between high and low workload on the basis of 2 min segments of EEG and eye related variables. A similar and not significantly different performance of 86% was reached using only EEG from single electrode location Pz.
•A technical enabler towards a fully deployed trajectory based operations environment.•Arrival traffic is sequenced with 4D closed-loop instructions in a tromboning.•The amount of CDOs is maximized ...and delay is minimized.•TMA entry time distribution greatly affects the number of neutral CDOs that can be flown.
This paper proposes to enhance the current tromboning paradigm with a four dimensional trajectory negotiation and synchronization process with the aim to maximise the number of neutral Continuous descent operations (CDOs, descents with idle thrust and no speed-brakes usage) achieved by the arriving traffic in terminal maneuvering areas (TMAs). An optimal control problem has been formulated and solved in order to generate a set of candidate CDO trajectories per aircraft, while a mixed-integer-linear programming model has been built in order to optimally assign routes of the arrival procedure and required times of arrival (RTAs) to the arriving traffic when still in cruise. The assessment has been performed for Frankfurt am Main airport (Germany), by using arrival traffic gathered from historical data. Results show that, after assigning an RTA and a route to every arriving aircraft, it is possible to maximize the number of aircraft performing CDOs while ensuring a safe time separation throughout the arrival procedure. For low traffic scenarios, the totality of traffic can be successfully scheduled, while for high traffic scenarios this is not the case and not all aircraft can be scheduled if neutral CDOs are flown. However, by assuming different arbitrarily defined arrival times to the TMA or by considering more additional shortcuts in the trombone procedure it is possible to increase the number of aircraft scheduled. Besides improving current operations in the short-mid term, the methodology presented in this paper could become a technical enabler towards a fully deployed trajectory based operations (TBO) environment.
Recently, urban air mobility (UAM), a new transportation system that can expand urban mobility from 2D to 3D, has been in the spotlight all over the world. For successful implementation of UAM, not ...only eVTOL aircraft development but also various systems such as UAM traffic management are required; however, research on these areas is still insufficient. Based on the BQA model, in this study, we introduce the balanced branch queuing approach (BBQA) model as a new approach control model that can improve operational efficiency by enabling the landing order to be changed more easily. Through simulation, its effectiveness was verified. The proposed BBQA achieved the identical airspace safety as the BQA model, in addition to showing a superior result to the SBA model in on-time performance (OTP). The vertiport airspace blueprint concept and approach control model proposed in this study are expected to play an important role in future studies in the area of air traffic management in UAM.
4D trajectory prediction is the core element of the future air transportation system. It aims to improve the operational ability and the predictability of air traffic. In this paper, a novel ...automated data-driven framework to deal with the prediction of Estimated Time of Arrival (ETA) on the runway at the entry point of Terminal Manoeuvring Area (TMA) is introduced. The proposed framework mainly consists of data preprocessing and machine learning models. Firstly, the dataset is divided, analyzed, cleaned, and estimated. Then, the flights are clustered into partitions according to different runway-in-use (QFU). Several candidate machine learning models are trained and selected on the corresponding dataset of each QFU. Feature engineering is conducted to transform raw data into features. After that, the experiments are performed on real ADS-B data in Beijing TMA with nested cross validation. By comparing the prediction performance on the preprocessed and un-preprocessed datasets, the results demonstrate that the proposed data preprocessing is able to improve the data quality. It is also robust to outliers, missing data, and noise. Finally, an ensemble learning strategy named stacking is introduced. Compared to other individual models, the stacked model has a more complex structure and performs best in ETA prediction. This fact reveals that the framework proposed in this study could make accurate and reliable ETA predictions.
•A refined preprocessing method is applied to handle complicated traffic patterns. It is capable of processing 4D trajectory data of all landing flights in TMA, even with outliers, missing points, and noise. This approach could be easily extended to other TMAs.•An automated ETA prediction model is developed to handle routine traffic at the entry of TMA. Machine learning strategies are utilized to deal with the highly-dense arrival trajectories in TMA.
Polarimetric radar observations increasingly are used to understand cloud microphysical processes, which is critical for improving their representation in cloud and climate models. In particular, ...there has been recent focus on improving representations of ice collection processes (e.g., aggregation and riming), as these influence precipitation rate, heating profiles, and ultimately cloud life cycles. However, distinguishing these processes using conventional polarimetric radar observations is difficult, as they produce similar fingerprints. This necessitates improved analysis techniques and integration of complementary data sources. The Midlatitude Continental Convective Clouds Experiment (MC3E) provided such an opportunity. Quasi‐vertical profiles of polarimetric radar variables in two MC3E stratiform precipitation events reveal episodic melting layer sagging. Integrated analyses using scanning and vertically pointing radar and aircraft measurements reveal that saggy bright band signatures are produced when denser, faster‐falling, more isometric hydrometeors (relative to adjacent times) descend into the melting layer. In one case, strong circumstantial evidence for riming is found during bright band sagging times. A bin microphysical melting layer model successfully reproduces many aspects of the signature, supporting the observational analysis. If found to be a reliable indicator of riming, saggy bright bands could be a proxy for the presence of supercooled liquid water in stratiform precipitation, which may provide important information for mitigating aircraft icing risks and for constraining microphysical models.
Key Points
Azimuthally averaged scanning polarimetric radar data reveal episodic melting layer sagging
Aircraft data show denser, faster‐falling particles (from riming in one case) lead to the signature
A bin model of melting snow with varying rime fraction reproduces the observed radar signatures
For air traffic management, trajectory prediction plays an important role as the predicted trajectory information is used in crucial tasks for the safety and efficiency of air traffic operations, ...such as conflict detection and resolution, scheduling, and sequencing. In this paper, we propose a framework for trajectory prediction in terminal airspace by combining a machine learning-based method and a physics-based estimation method. A trajectory prediction model based on machine learning is trained from historical surveillance data to represent the collective behavior of a set of flight trajectories, from which the data-driven prediction can be obtained as the expected future behavior of an incoming flight. A physics-based estimation algorithm called Residual-Mean Interacting Multiple Models (RM-IMM) then incorporates the machine learning prediction as a pseudo-measurement to account for the current motion of the aircraft. The proposed framework is tested, with real air traffic surveillance data, by predicting the future state information of the flights for real-time air traffic control applications. The results show that the proposed framework produces a greatly improved prediction accuracy compared to the two existing machine learning-based algorithms.
This paper presents a radar sensor package specifically developed for wide-coverage sounding and imaging of polar ice sheets from a variety of aircraft. Our instruments address the need for a ...reliable remote sensing solution well-suited for extensive surveys at low and high altitudes and capable of making measurements with fine spatial and temporal resolution. The sensor package that we are presenting consists of four primary instruments and ancillary systems with all the associated antennas integrated into the aircraft to maintain aerodynamic performance. The instruments operate simultaneously over different frequency bands within the 160 MHz-18 GHz range. The sensor package has allowed us to sound the most challenging areas of the polar ice sheets, ice sheet margins, and outlet glaciers; to map near-surface internal layers with fine resolution; and to detect the snow-air and snow-ice interfaces of snow cover over sea ice to generate estimates of snow thickness. In this paper, we provide a succinct description of each radar and associated antenna structures and present sample results to document their performance. We also give a brief overview of our field measurement programs and demonstrate the unique capability of the sensor package to perform multifrequency coincidental measurements from a single airborne platform. Finally, we illustrate the relevance of using multispectral radar data as a tool to characterize the entire ice column and to reveal important subglacial features.
This paper presents a data-driven approach for multi-scale characterization of the Brazilian airspace structure and air traffic operational performance from aircraft tracking data recorded by ...surveillance systems. Unsupervised learning is performed with a flight trajectory clustering analysis to automatically identify spatial traffic patterns in both the terminal and the en route airspace for major origin-destination pairs of the Brazilian air transportation system. Based on the as-flown route structure learned, quantitative metrics are developed to describe the structural efficiency of the airspace and the operational efficiency of the traffic flows. For this, actual flight trajectories are projected onto reference nominal trajectories in space and time. The results allowed for cross-route comparisons of air traffic flow efficiency across multiple flight phases as well as for the identification of causal factors for trajectory deviations from nominal routes. An interactive data analytics tool is also created to output performance statistics and air traffic visualizations. With the provision of a systematic data-driven approach for characterizing actual air traffic operations, the analytics framework is envisioned to assist airspace design and performance monitoring processes and to provide the basis for developing predictive capabilities in support of traffic flow management.
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