Terminal Airspace Research Articles

Prediction of runway configurations and airport acceptance rates for multi-airport system using gridded weather forecast

Accurate prediction of real-time airport capacity, a.k.a. airport acceptance rates (AARs), is key to enabling efficient air traffic flow management. AARs are dependent on selected runway configurations and both are affected by weather conditions. Although there have been studies tackling on the prediction of AARs or runway configurations or both, the prediction accuracy is relatively low and only single airport is considered. This study presents a data-driven deep-learning framework for predicting both runway configurations and AARs to support efficient air traffic management for complex multi-airport systems. The two major contributions from this work are 1) the proposed model uses assembled gridded weather forecast for the terminal airspace instead of an isolated station-based terminal weather forecast, and 2) the model captures the operational interdependency aspects inherent in the parameter learning process so that proposed modeling framework can predict both runway configuration and AARs simultaneously with higher accuracy. The proposed method is demonstrated with a numerical experiment taking three major airports in New York Metroplex as the case study. The prediction accuracy of the proposed method is compared with methods in current literature and the analysis results show that the proposed method outperforms all existing methods.

Operational efficiency analysis of Beijing multi-airport terminal airspace

Multi-airport systems are growing in number and size globally, despite being afflicted by known inefficiencies due to the interferences between the flows of neighbouring airports. A macroscopic empirical approach is proposed in this paper to estimate the capacity penalties and demonstrated by a numerical case study for Beijing, which is projected to become one of the busiest metroplexes in Asia. The Pareto envelopes of the theoretical and observed peak hour capacities are statistically analysed to quantify the penalties in a comparable metroplex and are subsequently modulated by a sigmoid correlation function. The analysis predicts the practical capacity of Daxing, the penalty incurred by the pre-existing Capital airport and by the total multi-airport system. Various findings are drawn and discussed, highlighting the needs for further research.

Analysis of Terminal Area Airspace Operation Status Based on Trajectory Characteristic Point Clustering

The terminal control area airspace system is one of the most complicated subsystems in the entire air traffic control system. With the continuous increase of air traffic volume, the utilization of its airspace resources has become a bottleneck restricting the sustainable development of the system. Monitor and obtain its operating status information and formulating corresponding mitigation measures are essential to improve the overall performance of the system. In order to achieve that, a new system analysis method based on clustering of trajectory characteristic point is proposed. The flight trajectories from radar position data are reconstructed at first. Followed by the method of extracting flight trajectory characteristic points including turning points, altitude transition points and speed adjustment points, all characteristic points are extracted. Then, K-means is applied for clustering according to their 3D space position, while the results are analyzed by introducing evaluation indexes based on air traffic control operation strategies. Numerical experiments show significant clustering characteristics and revealing the operation status of the terminal area airspace, which can help air traffic manager to identify defects in airspace operation and providing guidance for future airspace planning and flight procedure adjustment.

Concept of Operation for integrating RPAS into terminal airspace

Remotely Piloted Aircraft System (RPAS) is a capital issue for the majority of aviation actors nowadays. The integration of RPAS is an extremely demanding task that must be tackled by multiple standpoints: economic, social, technological or environmental among others. U-space is the answer from Europe to design the operation of multiple and different types of RPAS. U-space is a set of novel services designed to support efficient, safe and secure access to airspace, from the very low level to the upper airspace. This paper focuses on the terminal airspace to design the operational concept for the integration of RPAS in conjunction with conventional aircraft and general aviation. This work develops a holistic methodology to analyse the way RPAS integration affect crucial factors and tries to bring to the light different issues that can frustrate their integration. First, we focus on detailing the requirements specified by RPAS legislation. Second, all the requirements are gathered into different categories to perform further analysis. The categorisation identifies navigation, communication, surveillance, air traffic management and control, and safety as crucial categories (security is out of the scope of this study). The results provide for each category a proposal about how to solve the integration and implementation of RPAS in a TMA.

Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace

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.

Traffic synchronization in terminal airspace to enable continuous descent operations in trombone sequencing and merging procedures: An implementation study for Frankfurt airport

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.

Performance evaluation of terminal airspace system safety, delay and predictability of Muritala Mohammed International Airport, Ikeja Lagos, Nigeria

This paper outlines a multi-dimensional user-oriented performance metrics approach in evaluating the operation of the terminal airspace system to aid in the airport and airspace planning and decision making. Safety, delay and predictability metrics contribute to the analytical framework. From the findings, the occurrence of air incidence has a high severity level at departure, and arrival phases of flight, higher likelihood at the radar room and much of the incidences were as a result of faulty equipment and inherent absence of modern airspace infrastructure. Also, in Lagos terminal airspace, the number of incidences has no close correlation with the level of traffic complexity. Total schedule arrival delay ranges from 1-392 minutes representing an average of 7.8-17.9 minutes per aircraft that arrived Lagos airport at that period. Besides, the total approach contact time ranges from 1-57 minutes, translating to 4.6-7.1 minutes per aircraft. However, variability in arrival time of 1-5 minutes is common from published airline arrival scheduled time. In the same vein, the variability of 1-5 minutes is common from approach contact times of aircraft. These figures indicate sound arrival predictability signature for Lagos airport. Also, departure time variability above 30 minutes is familiar from the ATC clearance time for the various routes under study. However, there is about or more 25% variability of more than 15 minutes, and this indicates possible inconsistency of predicting departure times from the times ATC clearance was acquired. Above all, the predictability of departure times in Lagos airport is weak compared to those of the arrival. Taken by it, this may be a sign of airspace congestion or ATC deficiencies at the Lagos airport. This is an indication of the lack of users' confidence in Nigeria's air transport industry to deliver just-in-time service.

VEFP: visual evaluation of flight procedure in airport terminal

The optimization of terminal airspace can provide more airport capacity to meet the growing aviation demand. Improving flight procedures is an important prerequisite for optimizing airspace. The existing air-route network visualization cannot fully meet the assessment needs of the terminal flight procedure. In this research, we introduce an analysis tool, VEFP, that provides multiple visualizations based on unlabeled flight trajectory data. The system can help domain experts to evaluate the terminal flight procedure from multiple perspectives, respectively. First, we provide a time series-based statistical information view to help determine the usage status of the flight procedure per unit time. Second, we evaluated the controller’s use of space for flight procedures based on the location information in the data. Third, combined with the visualization method after data processing, the visual complexity is reduced and necessary details are displayed. Then, the users can directly observe the actual flight procedure. We evaluate flight procedures in actual use through cases and experiments and then discuss with users about the observations provided by the system. These results confirm that our system can help domain experts evaluate flight procedures.

Approach Airspace Utilization Rate Mode of Terminal Area in Blocky Distributed Hazardous Weather

Hazardous weather has a great impact on the airspace operation of the terminal area, which will lead to a reduction in the utilization of the airspace of the arrival operation. Therefore, it is necessary to evaluate the airspace utilization of the terminal area in hazardous weather. This paper analyzed the impact of blocky hazardous weather area on flight path, flight distance and flight time, and established a calculation model for the terminal area arrival airspace utilization rate in the blocky hazardous weather area based on the flow volume ratio based on the time and space constraints of the terminal area airspace operation. We selected a control terminal which was affected by the blocky hazardous weather area, then used the model to calculate the approach airspace utilization rate and compared it with the approach airspace utilization rate under normal weather conditions. The results show that the evaluation results of the established model can reflect the actual operating conditions.

Reactive Temporal Logic-Based Precursor Detection Algorithm for Terminal Airspace Operations

The air traffic management system is one of the most complex manmade systems, with stringent standards for safety and operational performance. Modern surveillance systems make available detailed flight and airport information through onboard and ground recording systems. These recorded datasets can be used for detecting and/or predicting anomalies that hinder safe and efficient operations. The prediction of an anomaly is performed by identifying events that precede the occurrence of an anomaly, which are called precursors. In this paper, we propose a precursor detection algorithm that can identify precursors for flight anomalies through data-driven models designed with surveillance data recorded in the terminal airspace. The proposed algorithm is demonstrated to detect precursors of flight anomalies in the terminal airspace around LaGuardia Airport in New York City using real traffic data obtained from the Airport Surface Detection Equipment–Model X and the terminal automation information service surveillance datasets.

UAS integration in congested terminal airspace: challenges posed to pilots

There is increasing demand to operate unmanned aircraft systems (UAS) in congested terminal environments, such as busy commercial airports. With this demand comes challenges to pilots. To identify these challenges, we conducted critical decision method (CDM) interviews with pilots. CDM is a cognitive task analysis method aimed at uncovering tacit cognitive challenges. Eight pilots from the U.S. were interviewed including four UAS pilots and four commercial pilots. Interviews were analyzed using thematic analysis, resulting in the identification of four categories of cognitive challenges: (i) noticing anomalies, (ii) diagnosing automation behavior, (iii) understanding when and how to intervene, and (iv) coordinating with air traffic control. In this paper, we describe each challenge, highlight real-world examples from our interviews, and provide some recommendations for addressing the implications of integrating UAS in congested terminal airspace.

Tactical Separation and Safety-Alerting System for Terminal Airspace

The provision of tactical alerts to aid air traffic controllers in providing separation assurance in terminal airspace is hindered by the complexity of the airspace, its operations, and flight procedures. A prototype automation system is studied that provides controllers with both separation and safety alerts based on or derived from the separation standard for terminal airspace. The system models flight trajectories heuristically with the use of merged intent information from readily available sources: area navigation departure procedures, flight-plan routes, and arrival nominal interior routes used in terminal automation systems. Flight vertical intent is modeled according to standard procedural restrictions, except when superseded by controller-issued altitude clearances. Importantly, flight trajectories are modeled for all aircraft, including those conducting visual approaches. New safety-alert thresholds for aircraft conducting visual approaches are studied. Performance of the system is evaluated through fast-time playback of recorded air traffic data from high-fidelity human-in-the-loop simulations and real-world operations in two terminal radar approach control facilities. The prototype system is found to produce a false-alert rate of 8% for separation alerts. The number and validity of safety alerts are studied by comparing with the current Conflict Alert system, showing that the false alerts of Conflict Alert are at 85% and they are avoided in the prototype system.

Count Models to Represent the Impacts of Weather and Infrastructure on Flight Disruptions

This paper explores the application of count models to represent the relationship between flight disruptions and weather. Throughout the world, flights are regularly disrupted by delays at airports and in the terminal airspace, and less frequently by diversions and cancelations. Many delay studies have been conducted for large American and European airports, in part due to the availability of high-quality data. However, such high-quality data is not as readily available for other airports throughout the world. In this study, excess-zero count models are built using a publicly available dataset for Iqaluit Airport (YFB) in Northern Canada, to determine the influence of different weather components on disruption counts. Visibility and crosswind speeds are shown to have the largest influence on flight disruptions. The models are also applied using Aviation System Performance Metrics (ASPM) flight data for Anchorage Airport (ANC) in Alaska; the data is systematically degraded to match completeness of the Iqaluit data to test the models. The results verify that an excess-zero model using incomplete data yields results similar to that of a count model with complete data, demonstrating that an excess-zero model can overcome data incompleteness to yield acceptable results. Although count models have been applied extensively in the transportation literature, the authors believe this to be the first application to flight disruptions, and the first quantitative model of operations at a northern Canadian airport. This paper demonstrates that challenges in data availability—the case for most airports throughout the world—can be addressed with novel statistical modeling applications, and thus, delay studies can be conducted for almost any airport.

Optimising aircraft arrivals in terminal airspace by mixed integer linear programming model

ABSTRACTAir traffic flow becomes denser and more complex within terminal manoeuvering areas (TMAs) due to rapid growth rates in demand. Effective TMA arrival management plays a key role in the improvement of airspace capacity, flight efficiency and air traffic controller performance. This study proposes a mixed integer linear programming model for aircraft landing problems with area navigation (RNAV) route structure using three conflict resolution and sequencing techniques together: flexible route allocation, airspeed reduction and vector manoeuver. A two-step mixed integer linear programming model was developed that minimises total conflict resolution time and then total airborne delay using lexicographic goal programming. Experimental results demonstrate that the model can obtain conflict-free and time optimal aircraft trajectories for RNAV route structures.

An intelligent recognition model for dynamic air traffic decision-making

Air traffic flow management system (ATFMS) is becoming increasingly important due to the rapid growth of air traffic and serious flight delay nowadays. To aware the air traffic flow density and identify the heat airspace in terminal areas of large hub airports is essential for an ATFMS. Due to numerous parameters in air traffic flow, traditional methods based on one single parameter fail to reflect the true complexity relationship between these parameters. This study aims to develop an intelligent air traffic flow heat airspace recognition model using advanced data science technique for establishing a real-time cloud map in the terminal airspace of airports, which attempts to use machine learning models to represent the complex relationship among these parameters. In the proposed intelligent recognition model, high dimensions of parameters (basic parameters, additional parameters and time parameters) are processed to achieve a comprehensive and accurate situation awareness for support dynamic air traffic decision-making. An aircraft trajectories points clustering method is developed to generate a 4D heat airspace map. The basic parameters and time parameters are used to identify the heat airspaces; the changes of additional parameters which influence the heat airspaces are identified and analyzed by use of grid graphs of flight trajectories; probability fitting graphs are used to verify accuracy of 4D results in order to support air traffic decision-making. A case study on Beijing International Airport (PEK) is conducted to test our model and has obtained two main research findings: there are two areas of PEK that have the high density and there are hot peaks at two different heights; flight trajectories and speed of trajectories also effect on the heat airspace. The study realizes that the proposed 4D heat airspace model is better for detailed and accurate information construction, expression of spatial changes, and visualization of multiple parameters of temporal and spatial density and range. It can assist the decisions on airspace allocation, and also have a definite reference meaning on alleviating the contradiction between the current air traffic demand and airspace resource constraints.

A two-stage robust optimisation for terminal traffic flow problem

Airport congestion witnesses potential conflicts: insufficient terminal airspace and delay propagation within scrambled the competition in the terminal manoeuvring area. Re-scheduling of flights is needed in numerous situations, heavy traffic in air segments, holding patterns, runway schedules and airport surface operations. Robust optimisation for terminal traffic flow problem, providing a practical point of view in hedging uncertainty, can leverage the adverse effect of uncertainty and schedule intervention. To avoid delay propagation throughout the air traffic flow network and reduce the vulnerability to disruption, this research adopts a two-stage robust optimisation approach in terminal traffic flow. It further enhances the quality of Pareto-optimality Benders-dual cutting plane based on core point approximation in the second stage recourse decision. The efficiency of the cutting plane algorithm is evaluated by a set of medium sized real-life scenarios. The numerical results show that the proposed scheme outperforms the well-known Pareto-optimal cuts in Benders-dual method from the literature.

A Deep Learning Approach for Aircraft Trajectory Prediction in Terminal Airspace

Current state-of-the-art trajectory methods do not perform well in the terminal airspace that surrounds an airport due to its complex airspace structure and the frequently changing flight postures of aircraft. Since an aircraft that takes off or lands in an airport must follow a specified procedure, this paper will learn a data-driven trajectory prediction model from many historical trajectories to improve the accuracy and robustness of trajectory prediction in the terminal airspace. A regularization method is utilized to reconstruct each aircraft trajectory to obtain a high-quality trajectory with equal time intervals and no noise. Furthermore, we formulate the 4D trajectory prediction problem as a sequence-to-sequence learning problem, and we propose a sequence-to-sequence deep long short-term memory network (SS-DLSTM) for trajectory prediction, which can effectively capture the long and short temporal dependencies and the repetitive nature among trajectories. The proposed model is composed of an encoding module and a decoding module, where the encoding mode realizes the feature representation of historical trajectories, while the decoding module accepts the output of the encoding module as its initial input and recursively outputs the predicted trajectory sequence. The proposed method is applied to a dataset for the terminal airspace in Guangzhou, China. The experimental results demonstrate that our approach has relatively high robustness and outperforms mainstream data-driven trajectory prediction methods in terms of accuracy.

Performance Evaluation of Terminal Airspace System Safety, Delay and Predictability of Muritala Mohammed International Airport, Ikeja Lagos, Nigeria

This paper outlines a multi-dimensional user-oriented performance metrics approach in evaluating the operation of the terminal airspace system to aid in the airport and airspace planning and decision making. Safety, delay and predictability metrics contribute to the analytical framework. From the findings, the occurrence of air incidence has a high severity level at departure, and arrival phases of flight, higher likelihood at the radar room and much of the incidences were as a result of faulty equipment and inherent absence of modern airspace infrastructure. Also, in Lagos terminal airspace, the number of incidences has no close correlation with the level of traffic complexity. Total schedule arrival delay ranges from 1 - 392 minutes representing an average of 7.8 - 17.9 minutes per aircraft that arrived Lagos airport at that period. Besides, the total approach contact time ranges from 1 - 57 minutes, translating to 4.6 - 7.1 minutes per aircraft. However, variability in arrival time of 1 - 5 minutes is common from published airline arrival scheduled time. In the same vein, the variability of 1 - 5 minutes is common from approach contact times of aircraft. These figures indicate sound arrival predictability signature for Lagos airport. Also, departure time variability above 30 minutes is familiar from the ATC clearance time for the various routes under study. However, there is about or more 25% variability of more than 15 minutes, and this indicates possible inconsistency of predicting departure times from the times Air Traffic Control (ATC) clearance was acquired. Above all, the predictability of departure times in Lagos airport is weak compared to those of the arrival. Taken by it, this may be a sign of airspace congestion or ATC deficiencies at the Lagos airport. This is an indication of the lack of users’ confidence in Nigeria’s air transport industry to deliver just-in-time service.

Rolling-Horizon Electric Vertical Takeoff and Landing Arrival Scheduling for On-Demand Urban Air Mobility

Urban air mobility with electric vertical takeoff and landing (eVTOL) vehicles is envisioned to become a fast and flexible urban transportation mode. Apart from technical challenges for eVTOLs regarding vehicle design and manufacturing, airspace design and traffic control mechanisms are most desired on the operation side. In particular, from an operational point of view, the arrival phase is expected to be the main bottleneck, with restricted vertiport resources, high air traffic density, frequent flight maneuvers, and limited eVTOL remaining battery energy, all leading to complex operational constraints. This work provides a framework to enable optimal and efficient on-demand eVTOLs arrivals in the context of on-demand urban air mobility. This paper investigates the throughput of a double-landing-pad vertiport by proposing a new vertiport terminal area airspace design and a novel rolling-horizon scheduling algorithm with route selection capability to compute the optimal required time of arrival for eVTOLs in a tactical manner. Finally, a case study on arrivals in a hexagonal vertiport network is performed to show the algorithm performance with different configurations. Our simulation results show that up to 50 s delay per eVTOL is expected during the commuter peak hours and less than 10 s delay is expected during off-peak hours.

Accelerated 129 Xe MRI morphometry of terminal airspace enlargement: Feasibility in volunteers and those with alpha-1 antitrypsin deficiency.

Multi-b diffusion-weighted hyperpolarized inhaled-gas MRI provides imaging biomarkers of terminal airspace enlargement including ADC and mean linear intercept (Lm ), but clinical translation has been limited because image acquisition requires relatively long or multiple breath-holds that are not well-tolerated by patients. Therefore, we aimed to accelerate single breath-hold 3D multi-b diffusion-weighted 129 Xe MRI, using k-space undersampling in imaging direction using a different undersampling pattern for different b-values combined with the stretched exponential model to generate maps of ventilation, apparent transverse relaxation time constant ( ), ADC, and Lm values in a single, short breath-hold; accelerated and non-accelerated measurements were directly compared. We evaluated multi-b (0, 12, 20, 30, and 45.5 s/cm2 ) diffusion-weighted 129 Xe /ADC/morphometry estimates using acceleration factor (AF = 1 and 7) and multi-breath sampling in 3 volunteers (HV), and 6 participants with alpha-1 antitrypsin deficiency (AATD). For the HV subgroup, mean differences of 5%, 2%, and 8% were observed between fully sampled and undersampled k-space for ADC, Lm , and values, respectively. For the AATD subgroup, mean differences were 9%, 6%, and 12% between fully sampled and undersampled k-space for ADC, Lm and values, respectively. Although mean differences of 1% and 4.5% were observed between accelerated and multi-breath sampled ADC and Lm values, respectively, mean ADC/Lm estimates were not significantly different from corresponding mean ADCM /LmM or mean ADCA /LmA estimates (all P > 0.60 , A = undersampled and M = multi-breath sampled). Accelerated multi-b diffusion-weighted 129 Xe MRI is feasible at AF = 7 for generating pulmonary ADC and Lm in AATD and normal lung.