A fuzzy modeling approach to optimize control and decision making in conflict management in air traffic control

@article{Lovato2018AFM,
  title={A fuzzy modeling approach to optimize control and decision making in conflict management in air traffic control},
  author={Agnaldo V. Lovato and Cristiano Hora Fontes and Marcelo Embiruçu and Ricardo de Ara{\'u}jo Kalid},
  journal={Comput. Ind. Eng.},
  year={2018},
  volume={115},
  pages={167-189}
}

Figures from this paper

A Hybrid Approach for Detecting and Resolving Conflicts in Air Traffic Routes

TLDR
The results show that the proposed approach is able to detect and remove longitudinal conflicts in advance, providing a potential tool to support decision-making, improve safety and optimize the use of airspace.

Geographical information system for air traffic optimization using genetic algorithm

TLDR
The purpose of this article is to replace the instructor with a Geographical Information System (GIS) solution combined with a genetic algorithm which, from a set of FPLs, will find the best solution to ensure on the one hand the safety of the aircraft but also minimizing the distance and the changes to be made.

Review of optimization and automation of air traffic control systems

TLDR
The objective of this paper is to review systematically current research in the literature about the automation and optimization of air traffic control system.

FUZZY MODEL OF THE OPERATIONAL POTENTIAL CONSUMPTION PROCESS OF A COMPLEX TECHNICAL SYSTEM

TLDR
The model of the operational potential consumption process was created and the results of the verification were presented, proving the adequacy of the model implementation in the case of industrial objects.

Radar Error Calculation and Correction System Based on ADS-B and Business Intelligent Tools

TLDR
It was found that it is possible to use it by a repetitive error measured ADS-B track like a reference track to calculate the error and in this way, it could be possible to reduce the uncertainty about the aircraft position.

Radar Error Calculation and Correction System Based on ADS-B and Business Intelligent Tools

TLDR
It was found that it is possible to use it by a repetitive error measured ADS-B track like a reference track to calculate the error and in this way, it could be possible to reduce the uncertainty about the aircraft position.

Optimization of air traffic management efficiency based on deep learning enriched by the long short-term memory (LSTM) and extreme learning machine (ELM)

TLDR
The bidirectional long short-term memory and extreme learning machines and ELM are used to design the structure of a deep learning network method and it can be said that the proposed method has a much higher air traffic management capacity in comparison to the previous methods.

References

SHOWING 1-10 OF 42 REFERENCES

Balance Modelling and Implementation of Flow Balance for Application in Air Traffic Management

TLDR
The FBM was developed as a model of analysis which determines the separation time between departures from terminals integrating the Brasilia Flight Information Region (FIR-BS), and distributes the slack capacity along the controlled airspace, in order to prevent or reduce traffic congestion in various sectors of FIR-BS.

TYPE-2 FUZZY LOGIC AND A CASE STUDY APPLIED TO AIR TRAFFIC CONTROL

TLDR
The development of a model structured according to type -2 fuzzy logic and based on human specialist knowledge applied to takeoff permission was developed and a methodology for the validation of the model was also developed.

On modeling the air traffic control coordination in the collision avoidance problem by mixed integer linear optimization

TLDR
A mixed integer linear optimization model is presented for providing a cooperative system between Air Traffic Control Officers who manage the airspace for aircraft conflict detection and resolution and is extended to cover the important problem of coordinating the decisions of the Air Traffic control Officers of different air sectors.

Aircraft terrain following flights based on fuzzy logic

TLDR
The fuzzy controller as presented in this work decides where and how the aircraft needs to change its altitude using fuzzy logic, which promises real‐time applications even for tough terrains in terms of shape and peculiarities.

Reinforcement learning agents to tactical air traffic flow management

TLDR
A comparative study of ATFM measures generated by a computational agent based on artificial intelligence (reinforcement learning) to avoid congestion or saturation in the air traffic control sectors due to a possible imbalance between demand and capacity.

A knowledge‐based conflict resolution tool for en‐route air traffic controllers

TLDR
A knowledge‐based decision support tool used for assisting en‐route air traffic controllers by generating resolutions for dual aircraft conflicts after being integrated into a model‐based conflict detection and conflict resolution system is developed.

Air holding problem solving with reinforcement learning to reduce airspace congestion

Summary The Air Holding Problem Module is proposed as a decision support system to help air traffic controllers in their daily air traffic flow management. This system is developed using an