An integrated approach to air traffic management to achieve trajectory based operations

Torres, S.; Delpome, Kelly L.

doi:10.1109/dasc.2012.6382325

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…Given the above flight plans, we then use the First-Come, First-Served approach to perform strategic conflict resolution, which is widely used in both ATM [25] and UTM [26], [27] field to prevent conflicts between the new flight and the previous flights. Recall that the flight plans are processed according to their request/submission order, rather than their scheduled take-off time, so this approach may be also referred to as First-Submitted/Requested, First-Served.…”

Section: E Pseudo Algorithmmentioning

confidence: 99%

Incorporating Optimization in Strategic Conflict Resolution for UAS Traffic Management

Tang

2023

IEEE Trans. Intell. Transport. Syst.

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This study presents an approach to incorporate optimisation in the strategic conflict resolution service for unmanned aircraft systems (UAS) traffic management. A conventional approach in line with the First-Come, First-Served (FCFS) principle is introduced, following the generation of two types of flight plans (i.e., linear and area operations) with uncertainty buffers further taken into account. This approach is based on iteratively checking the availability of the shared airspace volumes. Next, an optimisation model is formulated, using the same common airspace representation, aiming at minimising the overall delay and deviation to the equivalent FCFS solution (i.e., fairness concern), subject to operational constraints including a time-based separation minima. Some potential implementations are also envisioned for the optimisation model under plausible operational scenarios. Finally, simulation experiments are performed where five case studies are designed, including FCFS and optimisation, as well as their hybrid and batch uses depending on the flight plan submission time. Sensitivity analysis is conducted to assess the impact of some specific model assumptions. Results suggest that, compared to FCFS, a notable delay reduction can be achieved with optimisation incorporated, which is due to the FCFS prioritisation scheme that is often not efficient.

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Section: E Pseudo Algorithmmentioning

confidence: 99%

Incorporating Optimization in Strategic Conflict Resolution for UAS Traffic Management

Tang

2023

IEEE Trans. Intell. Transport. Syst.

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“…400NM before WPT final ); • From the eighth LFPL WPT to the EOC, the cruise section defines constant altitude and speed segments with a length of approximately 250NM (five LFPL segments). The cruise altitude and/or speed changes can occur at the LFPL WPTs [8,13,18,23,28,33,38,43,48]; and • The cruise phase altitude and speed values and the MACH speed for the final cruise segment (from WPT 53 to TOD) are selected at random: altitudes between 28,000ft and 43,000ft, and speeds between 0.68 and 0.9 MACH.…”

Section: Test Scenariomentioning

confidence: 99%

“…), and satisfies all constraints and regulations (4) . At the global level, the Air Traffic Management (ATM) system (5)(6)(7)(8)(9) performs the optimisation and validation. The objective is to ensure safe operations for all aircraft in that airspace (aircraft separation, compliance with navigation rules, regulations and policies, etc.)…”

Section: Introductionmentioning

confidence: 99%

New flight plan optimisation method utilising a set of alternative final point arrival time targets (RTA constraints)

Dancila

Botez

2021

Aeronaut. j.

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This study investigates a new aircraft flight trajectory optimisation method, derived from the Non-dominated Sorting Genetic Algorithm II method used for multi-objective optimisations. The new method determines, in parallel, a set of optimal flight plan solutions for a flight. Each solution is optimal (requires minimum fuel) for a Required Time of Arrival constraint from a set of candidate time constraints selected for the final waypoint of the flight section under optimisation. The set of candidate time constraints is chosen so that their bounds are contiguous, i.e. they completely cover a selected time domain. The proposed flight trajectory optimisation method may be applied in future operational paradigms, such as Trajectory-Based Operations/free flight, where aircraft do not need to follow predetermined routes. The intended application of the proposed method is to support Decision Makers in the planning phase when there is a time constraint or a preferred crossing time at the final point of the flight section under optimisation. The Decision Makers can select, from the set of optimal flight plans, the one that best fits their criteria (minimum fuel burn or observes a selected time constraint). If the Air Traffic Management system rejects the flight plan, then they can choose the next best solution from the set without having to perform another optimisation. The method applies for optimisations performed on lateral and/or vertical flight plan components. Seven proposed method variants were evaluated, and ten test runs were performed for each variant. For five variants, the worst results yielded a fuel burn less than 90kg (0.14%) over the ‘global’ optimum. The worst variant yielded a maximum of 321kg (0.56%) over the ‘global’ optimum.

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“…This method generates tailored wind forecasts based on the selected landing procedure, and on high-resolution regional forecasts; • New navigation strategies, which could yield greater flexibility in selecting a flight trajectory that is better adapted to the flight mission. By implementing the Trajectory-Based Operations (TBO) paradigm (6,7) , it would be possible for each aircraft to fly along a flight trajectory that is adapted to the specific flight, and atmospheric conditions; • New optimisation strategies, which would identify faster, and more precisely, globally optimal or near-optimal flight plans/trajectories.…”

Section: Introductionmentioning

confidence: 99%

New flight trajectory optimisation method using genetic algorithms

Dancila

Botez

2021

Aeronaut. j.

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This paper presents a new flight trajectory optimisation method, based on genetic algorithms, where the selected optimisation criterion is the minimisation of the total cost. The candidate flight trajectories evaluated in the optimisation process are defined as flight plans with two components: a lateral flight plan (the set of geographic points that define the flight trajectory track segments) and a vertical flight plan (the set of data that define the altitude and speed profiles, as well as the points where the altitude and/or speed changes occur). The lateral components of the candidate flight plans are constructed by selecting a set of adjacent nodes from a routing grid. The routing grid nodes are generated based on the orthodromic route between the flight trajectory’s initial and final points, a selected maximum lateral deviation from the orthodromic route and a selected grid node step size along and across the orthodromic route. Two strategies are investigated to handle invalid flight plans (relative to the aircraft’s flight envelope) and to compute their flight performance parameters. A first strategy is to assign a large penalty total cost to invalid flight profiles. The second strategy is to adjust the invalid flight plan parameters (altitude and/or speed) to the nearest limit of the flight envelope, with priority being given to maintaining the planned altitude. The tests performed in this study show that the second strategy is computationally expensive (requiring more than twice the execution time relative to the first strategy) and yields less optimal solutions. The performance of the optimal profiles identified by the proposed optimisation method, using the two strategies regarding invalid flight profile performance evaluation, were compared with the performance data of a reference flight profile, using identical input data: initial aircraft weight, initial and final aircraft geographic positions, altitudes and speed, cost index, and atmospheric data. The initial and final aircraft geographic positions, and the reference flight profile data, were retrieved from the FlightAware web site. This data corresponds to a real flight performed with the aircraft model used in this study. Tests were performed for six Cost Index values. Given the randomness of the genetic algorithms, the convergence to a global optimal solution is not guaranteed (the solution may be non-optimal or a local optima). For a better evaluation of the performance of the proposed method, ten test runs were performed for each Cost Index value. The total cost reduction for the optimal flight plans obtained using the proposed method, relative to the reference flight plan, was between 0.822% and 3.042% for the cases when the invalid flight profiles were corrected, and between 1.598% and 3.97% for the cases where the invalid profiles were assigned a penalty total cost.

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An integrated approach to air traffic management to achieve trajectory based operations

Cited by 6 publications

References 21 publications

Incorporating Optimization in Strategic Conflict Resolution for UAS Traffic Management

Incorporating Optimization in Strategic Conflict Resolution for UAS Traffic Management

New flight plan optimisation method utilising a set of alternative final point arrival time targets (RTA constraints)

New flight trajectory optimisation method using genetic algorithms

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