An Evolutionary Optimization Approach for Path Planning of Arrival Aircraft for Optimal Sequencing

Ahmed, Shohel; Alam, Sameer; Barlow, Michael

doi:10.1007/978-3-319-49049-6_1

Cited by 5 publications

(9 citation statements)

References 8 publications

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“…As these flights evolve through their plans and reach the sectors' limits, new ATCos are assigned for controlling them. However, a challenge currently faced is to maintain the workload 1 level below an acceptable threshold.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are situations in which such standard procedures cannot be followed (e.g., in case of high traffic density). In these cases, a highly challenging task due to complex maneuvers constraints performed by the ATCo is the sequencing of the aircraft during the approach, considering the arrival segment and the final approach [18] [1]. To accomplish this, the ATCo must conduct the aircraft in a manner to avoid conflict, i.e., to avoid disrespect to the minimum separation of aircraft, and to avoid flights through adverse weather conditions (e.g., flight through cumulonimbus -CB -, which are cloud formations that present a real impact on aviation [12]).…”

Section: Introductionmentioning

confidence: 99%

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Swarm-Based Optimization of Final Arrival Segments Considering the UAS Integration in the National Airspace System

et al. 2021

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In the past few years, there has been a growth in Unmanned Aircraft Systems (UAS) numbers in segregated airspace. However, although there is an interest in integrating large UAS in the National Airspace System (NAS), safety challenges regarding this insertion arise from the inclusion of new ways of reaching unsafe states. Although UAS may be used in different situations and brings several advantages to the airspace (e.g., efficiency), it may bring uncertainties due to the lack of familiarity of Air Traffic Controllers (ATCos) in these operations. Furthermore, the Terminal Maneuvering Area (TMA) is a critical control area generally established at the confluence of Air Traffic Service (ATS) routes in which the aircraft tend to be closer to each other. Besides, defining a final arrival segment for a set of aircraft in a complex environment is challenging. Thereupon, the main objective of this research is to propose a parallel swarmbased method for optimizing final aircraft arrival segment design (i.e., routes that connect the final sector to the Initial Approach Fix -IAF) considering the presence of aircraft of multiple Technology Maturity Levels (TML) -including the UAS. This is conducted from two perspectives: ATCo workload (which is related to safety) and sequencing duration (which is related to efficiency). Furthermore, different phases of UAS integration are considered using the Technology Maturity Levels (TMLs). Finally, the solutions consider airspace restrictions (e.g., minimum separation between aircraft and bad weather conditions). The experiments conducted show that this approach can build safe and efficient solutions, even in situations with many aircraft.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Swarm-Based Optimization of Final Arrival Segments Considering the UAS Integration in the National Airspace System

et al. 2021

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“…For the aircraft sequencing problem, some meta-heuristic techniques have been proposed in the literature [14][15][16][17]. These approaches consider maximizing runway capacity by finding optimal sequences for arrival and/or departure.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Co-Evolutionary Optimisation Model for Best-Fit Aircraft Sequence and Feasible Runway Configuration in a Multi-Runway Airport

2018

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A careful arrival and departure sequencing of aircraft can reduce the inter-arrival/departure time, thereby opening up opportunities for new landing and/or take-off slots, which may increase the runway throughput. This sequence when serviced with a suitable runway configuration may result in an optimal aircraft sequence with a runway configuration that can process the maximum number of aircraft within a given time interval. In this paper, we propose a Cooperative Co-evolutionary Genetic Algorithm (CCoGA) to find the combined solution of a best-fit sequence with a feasible runway configuration for a given traffic demand at an airport. The aircraft sequence and the runway configuration are modelled as individual species, which can cooperatively interact with each other. Therefore, we computationally evolve the best possible combination of aircraft sequence (arrival and departure) and the feasible runway configuration. The proposed CCoGA algorithm is evaluated for Chicago O’Hare International Airport runway layout and resulting configurations. Arrival and departure traffic demand is modelled through a Poisson distribution. Two different arrival/departure sequencing methods, i.e., constraint position shifting with one, two and N-position shifting and first come first serve, are modelled. Runway configuration and traffic sequence (arrivals and departure) are modelled as two species, which are evolved co-operatively, through the CCoGA algorithm, to achieve the optimal traffic sequencing with a feasible runway configuration. Time-space diagrams are presented for the best-evolved population of arrival-departure sequence and runway configuration to illustrate the possibility of using available departure slots between arrivals to maximize capacity. Arrival-departure capacity envelopes are then presented to illustrate the trade-off between the arrivals and departures, given a runway configuration for each sequencing method. Results demonstrate the high mutual dependence between arrival-departure sequence and the runway configuration, as well as its effect on overall runway capacity. The results also demonstrate the viability of using evolutionary computation-based methods for modelling and evaluating complex problems in the air transport domain.

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“…STAR) cannot be followed, for instance, in case of high tra c density. In these cases, a highly challenging task due to complex maneuvers constraints performed by the ATCo is the sequencing of the aircraft during the approach, considering the arrival segment and the final approach (ICAO, 2006) (AHMED;BARLOW, 2017). In order to accomplish this, the ATCo must conduct the aircraft in a manner to avoid conflict, i.e., to avoid disrespect to the minimum separation of aircraft, and to avoid flights through cumulonimbus (CB), which are cloud formations that present a real impact on aviation (FROMM et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Ahmed et al (AHMED;BARLOW, 2017) present an evolutionary method for optimizing the aircraft path planning algorithm in Terminal Maneuvering Area (TMA).…”

mentioning

confidence: 99%

Swarm-based optimization of final arrival segments considering the unmanned aircraft system integration into the non-segregated airspace.

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Carlos²

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An Evolutionary Optimization Approach for Path Planning of Arrival Aircraft for Optimal Sequencing

Cited by 5 publications

References 8 publications

Swarm-Based Optimization of Final Arrival Segments Considering the UAS Integration in the National Airspace System

Swarm-Based Optimization of Final Arrival Segments Considering the UAS Integration in the National Airspace System

A Cooperative Co-Evolutionary Optimisation Model for Best-Fit Aircraft Sequence and Feasible Runway Configuration in a Multi-Runway Airport

Swarm-based optimization of final arrival segments considering the unmanned aircraft system integration into the non-segregated airspace.

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