Terminal Maneuvering Area Research Articles

Estimating fuel consumption from radar tracks: a validation exercise using FDR and radar tracks from descent trajectories

Fuel consumption is one of the major considerations for both the impact of aviation in the environment and the cost of operations. This paper assesses the accuracy of a method capable of producing aircraft fuel estimates based on their 4D trajectory timestamps and the weather forecast. Fuel consumption estimates generated for 2448 descents are compared with the flight data recorder (FDR) values provided by the airline. Fuel consumption is estimated by taking the 4D trajectory from two different sources: the FDR system itself and surveillance radar tracks. In both cases, the Base of Aircraft Data (BADA) and a model that fits manufacturers’ performance data to polynomial functions are used to represent aircraft performance. Results obtained with the latter show that fuel usage could be estimated with an accuracy of 16 kg (4.8%) using the 4D trajectory as reported by the FDR system and 28 kg (7.8%) using surveillance radar observations. It is also observed that the BADA 3.6 model underestimates the fuel consumption, illustrating the need for an improved performance model in the terminal manoeuvring area. This method could be the cornerstone for the calculation of advanced performance indicators to measure flight efficiency (and environmental impact) in post-operational assessments, since it enables the estimation of the full state vector of an aircraft, including variables that are not explicitly included in the surveillance data (such the fuel, thrust, etc.).

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.

Aircraft rerouting and rescheduling in multi-airport terminal area under disturbed conditions

The disturbances, caused by resource competition, weather changes, and demand uncertainties, frequently occur and result in obvious fluctuations (e.g., the increase or decrease of demand and capacity) in demand and capacity. This work addresses the problem of aircraft rerouting and rescheduling in presence of disturbances, characterized by the increase or decrease of demand and capacity, at a multi-airport terminal maneuvering area (TMA). A multi-objective optimization model is proposed to minimize the total flight delays and total extra flying time, considering the aircraft wake turbulence separation, runway operation rules, air traffic control regulations, scheduling time windows, and airport/airspace capacity, etc. Based on the non-dominated sorting genetic algorithm (NSGA-II), we designed an efficient evolutionary algorithm to solve the proposed model and search the Pareto solutions. Five scenarios are designed to analyze and compare the operation performance under normal and disturbed conditions, using the classic First-Come-First-Served (FCFS) and optimized scheduling (OPTS) methods. A case study is conducted for Shanghai Pudong and Hongqiao Airports, and the computational results show that, compared with the FCFS method, the proposed OPTS method significantly performs a overall better performance in minimizing total flight delay and total extra flying time, while slightly inferior in optimizing the delay and extra flying time of one single flight.

A data-driven methodology for characterization of a terminal manoeuvring area in multi-airport systems

This paper proposes a methodology to characterize and evaluate the performance of the aircraft operation in complex systemized terminal manoeuvring area based on the study of its standard routes structure and their actual traffic.The methodology is composed of two main components. The first component identifies recurrent patterns by calculating the 4D adherence to the standard routes and flight plan. This process is based on the comparison of the Spatio-Temporal (S-T) differences between the actual and planned trajectories.The second component identifies and characterizes concurrence events based on the analysis of the standard routes and the along-track deviation derived from the first component to analyze the causes that produce recurrent patterns in the terminal airspace.The developed framework is applied to a study case of a representative Multi-Airport System (MAS). The quantitative effectiveness of the framework is derived by simulations using historical traffic data samples of the London Terminal Manoeuvring Area (LTMA).

STAM-based methodology to prevent concurrence events in a Multi-Airport System (MAS)

Pre-tactically managing the imbalances of the network and organizing the resources of the airspace is one of the main objectives of Dynamic Demand & Capacity Balancing (dDCB). Introducing Short-term Air Traffic Flow and Capacity Management (STAM) measures such as minor ground delays or slight speed adjustments applied to a selected number of flights on the day of operations, supports the dDCB process by reducing the traffic complexity and the probability of Air Traffic Controllers interventions. This paper introduces a rolling horizon methodology for detecting concurrence events, analysing their trajectory interdependencies and applying a strategic mitigation measure that shifts the Estimated Take-Off Time (ETOT) within the assigned Calculated Take-Off Time (CTOT) window. The methodology proposes a collaborative decision-making process for a better coordination of departures from airports feeding airspace volumes with high air traffic demand. The experimental part of this paper aims at showing the potential benefits of such a STAM procedure by applying the methodology to distribute the demand of a sector located in the London Terminal Manoeuvring Area (LTMA). Experiments have considered regulated traffic scenarios where ETOT were allocated in order to reduce departure time uncertainties. Lastly, it is analysed the impact of the parameters used by the methodology to calculate the mitigation measures: safe separation criteria, look-ahead time and number of coordinated airports.

Integration methods for aircraft scheduling and trajectory optimization at a busy terminal manoeuvring area

This paper deals with the problem of efficiently scheduling take-off and landing operations at a busy terminal manoeuvring area (TMA). This problem is particularly challenging, since the TMAs are becoming saturated due to the continuous growth of traffic demand and the limited available infrastructure capacity. The mathematical formulation of the problem requires taking into account several features simultaneously: the trajectory of each aircraft should be accurately predicted in each TMA resource, the safety rules between consecutive aircraft need to be modelled with high precision, the aircraft timing and ordering decisions have to be taken in a short time by optimizing performance indicators of practical interest, including the minimization of aircraft delays, travel times and fuel consumption. This work presents alternative approaches to integrate various modelling features and to optimize various performance indicators. The approaches are based on the resolution of mixed-integer linear programs via dedicated solvers. Computational experiments are performed on real-world data from Milano Malpensa in case of multiple delayed aircraft. The results obtained for the proposed approaches show different trade-off solutions when prioritizing different indicators.

Departure and Arrival Routes Optimization Near Large Airports

The bottleneck of today’s airspace is the Terminal Maneuvering Areas (TMA), where aircraft leave their routes to descend to an airport or take off and reach the en-route sector. To avoid congestion in these areas, an efficient design of departure and arrival routes is necessary. In this paper, a solution for designing departure and arrival routes is proposed, which takes into account the runway configuration, the surroundings of the airport and operational constraints such as limited slopes or turn angles. The routes consist of two parts: a horizontal path in a graph constructed by sampling the TMA around the runway, to which is associated a cone of altitudes. The set of all routes is optimized by the Simulated Annealing metaheuristic. In the process and at each iteration, each route is computed by defining adequately the cost of the arcs in the graph and then searching a path on it. The costs are chosen so as to avoid zigzag behaviors as much as possible. Two tests were performed, one on an instance taken from the literature and the other on an artificial problem designed specifically to test this approach. The obtained results are satisfying with regard to the current state of air operations management and constraints.

Integer Programming–Based Airspace Sectorization for Terminal Maneuvering Areas with Convex Sectors

In this paper an airspace sectorization framework for terminal maneuvering areas based on mixed integer programming is presented. It incorporates an airspace complexity representation, as well as various constraints on the sectors’ geometry, for example, the requirement that points demanding increased attention from air traffic controllers should lie in the sector’s interior to allow for enough time to resolve possible conflicts. The method can enforce convex sectors. In contrast to earlier integer/constraint programming approaches, which used synthesis methods with variables per elementary airspace piece that were glued together to form sectors, the integer programming formulation uses a variable per potential edge on the sector boundary. It is also the first step toward an integrated design of routes, the resulting complexity, and a sectorization. This paper presents results for Stockholm Arlanda airport and compares the integer programming results to convex sectorizations obtained by enumerating all possible topologies for a given number of sectors. This yields a proof-of-concept for the application of this highly flexible approach to terminal maneuvering areas.

Design of a conflict-detection air traffic control tool for the implementation of continuous climb operations: A case study at Palma TMA

Continuous climb operation is an operational concept that allows airlines to perform an optimal departing trajectory avoiding air traffic control segregation requirements. This concept implies the design and integration of air traffic flows for the sake of safety performance. This paper designs a new conflict-detection air traffic control tool based on the blocking-area concept, characterises the conflict probability between air traffic flows and assesses the impact of continuous climb operation integration in a terminal manoeuvring area. In this paper, a conflict is set out by the infringement of vertical and longitudinal separation minima and coincides with the probability of air traffic control tool usage. Moreover, this research discusses two different approaches for the conflict-detection air traffic control tool: a static approach considering nominal continuous climb operations and landing trajectories, and a dynamic approach that assesses 105 continuous climb operations and landing trajectories. Finally, the air traffic control tool is implemented using Palma TMA data and proves that out of 11 intersections (between departing and landing routes), solely 4 generate vertical separation infringements. The conflict probability between continuous climb operations and arrivals is less than 10−5. Except for one intersection, that is roughly 10−2, similar to current air traffic control intervention designed levels. Therefore, results conclude the viability of the conflict-detection air traffic control tool and continuous climb operations integration.

A framework for integrated terminal airspace design

ABSTRACTRoute planning and airspace sectorisation are two central tasks in air traffic management. Traditionally, the routing and sectorisation problems were considered separately, with aircraft trajectories serving as input to the sectorisation problem and, reciprocally, sectors being part of the input to the path finding algorithms.In this paper we propose a simultaneous design of routes and sectors for a transition airspace. We compare two approaches for this integrated design: one based on mixed integer programming, and one Voronoi-based model that separates potential “hotspots” of controller activity resulting from the terminal routes.We apply our two approaches to the design of Stockholm Terminal Maneuvering Area.

Arrival Traffic Separations Modelling By Using Continuous Probability Distributions

This article presents a method that allows to analyze selected aspects of past arrival traffic by modelling distributions of time separations of arriving aircraft in a chosen navigationpoint of Terminal Manoeuvring Area with the use of continuous probability distributions. Modelling arriving aircraft time separations distribution with continuous probability density functions allows to apply various mathematical tools to analyze separations distributions. Moreover, by comparing distributions parameters, quantitative analysis of separations for days with various arrival traffic intensity can be performed. Assumptions, mathematical model, application in the exemplary experimental scenario with an airport and days with low and high traffic intensity, and results are presented in this article. Real air traffic data was used for the experimental scenario. Outcomes show that the method can be used for air traffic post-analysis, e.g assessment of maintaining separation.

Integrated optimization of terminal maneuvering area and airport at the macroscopic level

Airports and surrounding airspaces are limited in terms of capacity and represent the major bottlenecks of the air traffic management system. This paper addresses the problems of terminal airspace management and airport congestion management at the macroscopic level through the integrated control of arrivals and departures. Conflict detection and resolution methods are applied to a predefined terminal route structure. Different airside components are modeled using network abstraction. Speed, arrival and departure times, and runway assignment are managed by using an optimization method. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on case studies of Paris Charles De-Gaulle airport show some potential improvements: First, when the airport capacity is decreased, until a certain threshold, the overload can be mitigated properly by adjusting the aircraft entry time in the Terminal Maneuvering Area and the pushback time. Second, landing and take-off runway assignments in peak hours with imbalanced runway throughputs can significantly reduce flight delays. A decrease of 37% arrival delays and 36% departure delays was reached compared to baseline case.

Airport Ground Movement Problem: Minimization of Delay and Pollution Emission

In this paper, we deal with the problem of routing and scheduling aircraft ground movement operations. We implement two objective functions: the minimization of total routing taxiing delay and the minimization of pollution emission in terms of total waiting time the engines are turned on. We use alternative graph model to represent the routing solution and to solve the airport ground movement problem. We consider the case study of malpensa terminal maneuvering area, Italy, which implies a peculiar traffic flow management due to its complex layout, where runways and taxi network intersect in many points. Experimental results based on real data are reported, which are promising both for the total routing taxiing delay minimization and the reduction of the pollution emission.

Trajectory clustering of air traffic flows around airports

We present a new approach to separate air traffic trajectories in an area constrained by operational procedures. This technique is applied on a set of real trajectories in Toulouse terminal manoeuvring area (TMA). The resulting clusters foster good understanding of the structure of traffic and of how controllers schedule landings at Toulouse–Blagnac airport; on the other hand, a group of peculiar trajectories emerge with useful information calling for further analysis and paving the way for a probabilistic approach to risk assessment in air traffic safety.

Fuel-Efficient Trajectories Traffic Synchronization

Continuous descent operations (CDOs) with required times of arrival (RTA) have been identified as a potential solution for reducing the environmental footprint of aviation in the terminal maneuvering area without compromising capacity. This paper assesses the feasibility of replacing current air traffic control sequencing and merging techniques, mainly based on path stretching and air holding, by a control based on RTA over metering fixes on known and fixed arrival routes. Because the remaining distance to the runway threshold is always known by the aircraft crew, this would allow engine-idle CDOs that do not require speed-brake usage and where only elevator control is used to meet the RTA. The assessment has been performed for Barcelona-El Prat Airport (Spain) using historical traffic demand data. The earliest and latest trajectories at a metering fix for each inbound aircraft were computed assuming engine-idle CDOs. Given the attainable RTA window for each aircraft, the aircraft sequencing problem was solved. The results show that assigning RTA allows optimizing the landing sequence when air traffic is low. For scenarios with high-traffic loads and late RTA assignments, path stretching still was found to be necessary. The minimum distance from the runway where inbound aircraft should receive the RTA to fully remove any radar vectoring was also analyzed. It was demonstrated that the assignment of RTA well before starting the descent would favor to enable full CDOs.

A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area

4D trajectory prediction is the core element of future air transportation system, which is intended to improve the operational ability and the predictability of air traffic. In this paper, we introduce a novel hybrid model to address the short-term trajectory prediction problem in Terminal Manoeuvring Area (TMA) by application of machine learning methods. The proposed model consists of two parts: clustering-based preprocessing and Multi-Cells Neural Network (MCNN)-based prediction. Firstly, in the preprocessing part, after data cleaning, filtering and data re-sampling, we applied principal Component Analysis (PCA) to reduce the dimension of trajectory vector variable. Then, the trajectories are clustered into several patterns by clustering algorithm. Using nested cross validation, MCNN model is trained to find out the appropriate prediction model of Estimated Time of Arrival (ETA) for each individual cluster cell. Finally, the predicted ETA for each new flight is generated in different cluster cells classified by decision trees. To assess the performance of MCNN model, the Multiple Linear Regression (MLR) model is proposed as the comparison learning model, and K-means++ and DBSCAN are proposed as two comparison clustering models in preprocessing part. With real 4D trajectory data in Beijing TMA, experimental results demonstrate that our proposed model MCNN with DBSCAN in preprocessing is the most effective and robust hybrid machine learning model, both in trajectory clustering and short-term 4D trajectory prediction. In addition, it can make an accurate trajectory prediction in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) with regards to comparison models.

Conflict-free arrival and departure trajectory planning for parallel runway with advanced point-merge system

In this paper, an efficient trajectory planning system is proposed to solve the integration of arrivals and departures on parallel runways with a novel route network system. Our first effort is made in designing an advanced Point Merge (PM) route network named Multi-Level Point Merge (ML-PM) to meet the requirements of parallel runway operations. Then, more efforts are paid on finding a complete and efficient framework capable of dynamically modelling the integration of arrival and departure trajectories on parallel runways, modelling the conflict detection and resolution in presence of curved trajectory and radius-to-fix merging process. After that, a suitable mathematical optimization formulation is built up. Receding Horizon Control (RHC) and Simulated Annealing (SA) algorithms are proposed to search the near-optimal solution for the large scale trajectories in routine dense operations. Taking Beijing Capital International Airport (BCIA) as a study case, the experimental results show that our system shows good performances on the management of arrivals and departures. It can automatically solve all the potential conflicts in presence of dense traffic flows. With its unique ML-PM route network, it can realize a shorter flying time and a near-Continuous Descent Approach (CDA) descent for arrival aircraft, an economical climbing for departure aircraft, an easier runway allocation together with trajectory control solutions. It shows a good and dynamic sequencing efficiency in Terminal Manoeuvring Area (TMA). In mixed ML-PM mode, under tested conditions, our proposed system can increase throughput at BCIA around 26%, compared with baseline. The methodology defined here could be easily applied to airports worldwide.

Real-Time UAS Guidance for Continuous Curved GNSS Approaches

This paper presents new efficient guidance algorithms allowing Unmanned Aircraft Systems (UAS) to avoid a variety of Global Navigation Satellite System (GNSS) continuity and integrity performance threats detected by an Aircraft Based Augmentation System (ABAS). In particular, the UAS guidance problem is formulated as an optimal control-based Multi-Objective Trajectory Optimization (MOTO) problem subject to suitable dynamic and geometric constraints. Direct transcription methods of the global orthogonal collocation (pseudospectral) family are exploited for the solution of the MOTO problem, generating optimal trajectories for curved GNSS approaches in real-time. Three degrees-of-freedom aircraft dynamics models and suitable GNSS satellite visibility models based on Global Positioning System (GPS) constellation ephemeris data are utilised in the MOTO solution algorithm. The performance of the proposed MOTO algorithm is evaluated in representative simulation case studies adopting the JAVELIN UAS as the reference platform. The paper focusses on descent and initial curved GNSS approach phases in a Terminal Maneuvering Area (TMA) scenario, where multiple manned/unmanned aircraft converge on the same short and curved final GNSS approach leg. The results show that the adoption of MOTO based on pseudospectral methods allows an efficient exploitation of ABAS model-predictive augmentation features in online GNSS guidance tasks, supporting the calculation of suitable arrival trajectories in 7 to 16 s using a normal PC.

Uncertainty Management at the Airport Transit View

Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept. Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system’s robustness. The methodology is validated through a case study at the Adolfo Suárez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance.

Characterization and prediction of the airport operational saturation

This paper develops a functional analysis of the aircraft flow through the airport operational framework, focusing on the airspace-airside integrated system. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Maneuvering Area (E-TMA) concept, so inbound and outbound timestamps can be considered. Aircraft operations are characterized by several temporal milestones, which arise from the combination of a Business Process Model (BPM) for the aircraft flow and the Airport Collaborative Decision Making (A-CDM) methodology. This timestamp approach allows us to study the successive hierarchical tasks. The objective is to establish a taxonomy that classifies the system's capacity to “receive and transmit” aircraft streams with adherence to the expected schedule. By considering the accumulated delay across the different processes and its evolution, several indicators are proposed to evaluate the system's level of saturation and its ability to ensure an appropriate aircraft flow in terms of time-efficiency. Finally, the relationships between the factors that influence the aircraft flow are evaluated to create a probabilistic graphical model, using a Bayesian Network (BN) approach. This model predicts outbound delays given the probability of having different values at the causal control variables. The methodology is developed and validated through a case study at Adolfo Suárez Madrid-Barajas Airport (LEMD): a collection of nearly 34,000 turnaround operations (registered at the peak traffic months of 2016) is used to statistically determine the aircraft path characteristics. The contribution of the paper is twofold: it presents a novel methodological approach to evaluate and predict the system's state at the rotation stage and it also provides insights on the interdependencies between factors influencing performance.