Planning efficient 4D trajectories in Air Traffic Flow Management.

Sasso, Veronica Dal; Fomeni, Franklin Djeumou; Lulli, Guglielmo; Zografos, Konstantinos G.

doi:10.1016/j.ejor.2019.01.039

Cited by 36 publications

(10 citation statements)

References 19 publications

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“…As there are multiple alternative flight paths for each drone, a multi-path planning algorithm is required. Some algorithms, such as pseudo-spectral method [33], tabu search algorithm [34] and simulated annealing algorithm [35], which begin with only one initial solution fail to satisfy the demand [36]. Besides, the final optimal solution is sensitive to the initial solution, which increase the difficulty of getting the optimal solution.…”

Section: Multi-path Planning By Clustering Improved Ant Colony Optimi...mentioning

confidence: 99%

Swarm-Based 4D Path Planning For Drone Operations in Urban Environments

Low

Pang

et al. 2021

IEEE Trans. Veh. Technol.

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Drones have a wide range of applications in urban environments as they can both enhance people's daily activities and commercial activities through various operations and deployments. With the increasing number of drones, flight safety and efficiency become the main concern, and effective drone operations can make a difference. Accordingly, 4D path planning for drone operations is the focus of this paper, and the swarm-based method is proposed to solve this complicated optimization problem. Under the framework of 'AirMatrix', the problem is solved in two levels, i.e., 3D path planning for a single drone and conflict resolution among drones. In the multipath planning level, multiple alternative flight paths for each drone are generated to increase the acceptance rate of a flight request. The constraints on a single flight path and two different flight paths are considered. The goal is to obtain several different short flight paths as alternatives. A clustering improved ant colony optimization (CIACO) algorithm is employed to solve the multi-path planning problem. The crowding mechanism is used in clustering, and some improvements are made to strengthen the global and local search ability in the early and later phases of iterations. In the task scheduling level, the conflicts between two drones are defined in two circumstances. One is for the time interval of passing the same path point, another one is for the right-angle collision between two drones. A three-layer fitness function is proposed to maximize the number of permitted flights according to the safety requirement, in which the airspace utilization and the operators' requests are both considered. A 'cross-off' strategy is developed to calculate the fitness value, and a 'distributed-centralized' strategy is applied considering the task priorities of drones. A genetic algorithm (GA)-based task scheduling algorithm is also developed according to the characteristic of the established model. Simulation results demonstrate that 4D flight path of each drone can be generated by the proposed swarmed-based algorithms, and safe and efficient drone operations in a specific airspace can be ensured.

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Section: Multi-path Planning By Clustering Improved Ant Colony Optimi...mentioning

confidence: 99%

Swarm-Based 4D Path Planning For Drone Operations in Urban Environments

Low

Pang

et al. 2021

IEEE Trans. Veh. Technol.

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“…All these schemes relate to TMIs that are implemented in the case of capacity reduction. A prioritization scheme has also been developed for the routing of flights across an airspace network (Dal Sasso et al 2019). Here, an optimization model and heuristic algorithm are proposed, which attempt to minimize various objectives based on the deviation of a flight from the airline's ideal route and according to a ranking by each airline of all of its flights.…”

Section: Iata Slot-allocation Process Andmentioning

confidence: 99%

A Slot-Scheduling Mechanism at Congested Airports that Incorporates Efficiency, Fairness, and Airline Preferences

Fairbrother

Zografos

Glazebrook

2020

Transportation Science

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Congestion is a problem at airports where capacity does not meet demand. At many such airports, airlines must request time slots for the purpose of landing or take off. Given the imbalance between demand and capacity, slot requests cannot always be scheduled as requested. The difference between the requested and allocated time slots is called displacement. Minimization of the total displacement is a key slot-scheduling objective and expresses the efficiency of the slot-scheduling process. Additionally, fairness has been proposed as a slot-scheduling criterion. Fairness relates to the allocation of the total schedule displacement among the various airlines. Single-and multiobjective models have been proposed for slot scheduling. However, currently the literature lacks models that incorporate the preferences of airlines regarding the allocation of displacement to their flights. This paper proposes a two-stage mechanism for the scheduling of slots at congested airports. The proposed mechanism considers efficiency and fairness objectives and incorporates the preferences of airlines in allocating the total displacement associated with the flights of each airline. The first stage of the mechanism constructs a reference schedule that is fair to the participating airlines. In the second stage, the airlines specify how the displacement allocated to them in the reference schedule should be distributed among their requests. The mechanism then adjusts the fair reference schedule to meet as many of these preferences as possible. The development and implementation of the proposed slotscheduling mechanism is demonstrated using real data from a coordinated airport and simulated displacement preference data. The proposed slot-scheduling mechanism provides useful information to decision makers regarding the equity-efficiency trade-off and enhances the transparency and acceptability of the slot-scheduling outcome.

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“…In recent years, more and more people choose to take planes as a means of travel based on the convenience and time-saving advantages of planes, which has led to a sharp increase in air traffic density, which has caused great contradictions with the basically unchanged available airspace resources for civil aviation. At the same time, when airspace resources are affected by emergencies such as bad weather, large-scale flight delays will also be caused, which has become a major air traffic problem (Dal Sasso et al ., 2019; Wang and Wang, 2019) With the increasingly serious problem of flight delays, airlines not only suffer huge economic losses but also cause bad social impacts. Odoni (1987) comprehensively expounded the basic concepts, main issues and research fields of air traffic flow management (ATFM) for the first time and put forward the idea of minimizing flight delay costs by rescheduling flight plans.…”

Section: Introductionmentioning

confidence: 99%

Collaborative slot secondary allocation based on flight wave operation

Chen

Yang

et al. 2022

IJICC

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PurposeFlights are often delayed owing to emergencies. This paper proposes a cooperative slot secondary assignment (CSSA) model based on a collaborative decision-making (CDM) mechanism, and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.Design/methodology/approachTaking passenger delays, transfer delays and flight cancellation delays into account comprehensively, the total delay time is minimized as the objective function. The model is verified by a linear solver and compared with the first come first service (FCFS) method to prove the effectiveness of the method. An improved adaptive partheno-genetic algorithm (IAPGA) using hierarchical serial number coding was designed, combining elite and roulette strategies to find pareto solutions.FindingsComparing and analyzing the experimental results of various scale examples, the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time, and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.Originality/valueBased on the actual situation, this paper considers the operation mode of flight waves. In addition, the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness, which can provide airlines with reasonable decision-making opinions when reassigning slot resources.

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Planning efficient 4D trajectories in Air Traffic Flow Management.

Cited by 36 publications

References 19 publications

Swarm-Based 4D Path Planning For Drone Operations in Urban Environments

Swarm-Based 4D Path Planning For Drone Operations in Urban Environments

A Slot-Scheduling Mechanism at Congested Airports that Incorporates Efficiency, Fairness, and Airline Preferences

Collaborative slot secondary allocation based on flight wave operation

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