Intelligent air traffic controller simulation using artificial neural networks

Kulkarni, Vinayak B.

doi:10.1109/iic.2015.7150897

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…Air traffic, and departure, a simulator via neutral network was designed to control air traffic and landing clearance. In a similar study, artificial neural networks have been applied to maintain the optimal distance between two airplanes during landing [15]. An alternative simulation model was constructed by Netjasov et.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of a Mission-Based Flight Priority System

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Yıldız,

Koçak

2024

International Journal of Advances in Engineering and Pure Sciences

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Purpose: The purpose of this study is to develop a mission-based flight priority system that decides which aircraft would match with which airborne operation, and determines a sequence of take-off for those airplane-operation peers. Both peers and take-off orders are specified by minimizing total operation cost which includes fuel cost, waiting cost and penalty cost for missed missions. The aim of this system is to create a cost effective, fast and efficient decision-making tool for allocating operation-aircraft assignments and determining the sequence of take-off, especially in emergency cases. Methodology: An integer programming model that minimizes the total cost are formulated. Four scenarios are designed to assess the performance of the system. The system, which includes five aircrafts and ten airborne operations, was revealed in the study. Integer programming is used while modeling the system and the Branch-and-Bound algorithm is used as the solution algorithm. The optimization algorithm was developed in MATLAB. Findings: Both emergency scenarios and normal scenarios are maintained with the purpose of examining the behaviors and the result of the system under different conditions. It is believed that system have given the appropriate sequence and matchup for air vehicle-operation peers. Originality: Since the integration of airplane-mission assignment and determining take-off sequence is rare in the literature, our study may be considered as a new approach. Therefore, in order to bring a new perspective, an optimization system related to the determination of flight priority and mission assignment was brought in this study.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of a Mission-Based Flight Priority System

Yaylalı,

Yıldız,

Koçak

2024

International Journal of Advances in Engineering and Pure Sciences

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“…Isaacson et al [38] proposed a knowledge-based conflict resolution process that allows predictive conflicts to be resolved in a manner consistent with controller practices: including prioritization of resolution strategies and multiple degrees of freedom blending to achieve separation. Tran et al [39] built an AI system as a digital assistant to support ATCOs in resolving potential conflicts. The proposed system consisted of two core components: one was an intelligent interaction conflict solution that acquired ATCOs' preferences, the other was an AI agent that used reinforcement learning (RL).…”

Section: Ai For Atsmentioning

confidence: 99%

Review on artificial intelligence techniques for improving representative air traffic management capability

Tang

Liu

Pan

2022

J. of Syst. Eng. Electron.

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The use of artificial intelligence (AI) has increased since the middle of the 20th century, as evidenced by its applications to a wide range of engineering and science problems. Air traffic management (ATM) is becoming increasingly automated and autonomous, making it lucrative for AI applications. This paper presents a systematic review of studies that employ AI techniques for improving ATM capability. A brief account of the history, structure, and advantages of these methods is provided, followed by the description of their applications to several representative ATM tasks, such as air traffic services (ATS), airspace management (AM), air traffic flow management (ATFM), and flight operations (FO). The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages: (i) these methods provide alternative approaches to conventional physical modeling techniques, (ii) these methods do not require knowing relevant internal system parameters, (iii) these methods are computationally more efficient, and (iv) these methods offer compact solutions to multivariable problems. In addition, this review offers a fresh outlook on future research. One is providing a clear rationale for the model type and structure selection for a given ATM mission. Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission. These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

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“…Apart from en-route airspace, machine learning methods have also been applied in terminal airspace. For example, in [22], a simulator was designed which can simulate control of air traffic and landing clearance and departure by using backpropagation network based on various controlling parameters, but for single-runway only.…”

Section: Introductionmentioning

confidence: 99%

An Air Traffic Controller Action Extraction-Prediction Model Using Machine Learning Approach

2020

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In air traffic control, the airspace is divided into several smaller sectors for better management of air traffic and air traffic controller workload. Such sectors are usually managed by a team of two air traffic controllers: planning controller (D-side) and executive controller (R-side). D-side controller is responsible for processing flight-plan information to plan and organize the flow of traffic entering the sector. R-side controller deals with ensuring safety of flights in their sector. A better understanding and predictability of D-side controller actions, for a given traffic scenario, may help in automating some of its tasks and hence reduce workload. In this paper, we propose a learning model to predict D-side controller actions. The learning problem is modeled as a supervised learning problem, where the target variables are D-side controller actions and the explanatory variables are the aircraft 4D trajectory features. The model is trained on six months of ADS-B data over an en-route sector, and its generalization performance was assessed, using crossvalidation, on the same sector. Results indicate that the model for vertical maneuver actions provides highest prediction accuracy (99%). Besides, the model for speed change and course change action provides predictability accuracy of 80% and 87%, respectively. The model to predict the set of all the actions (altitude, speed, and course change) for each flight achieves an accuracy of 70% implying for 70% of flights; D-side controller’s action can be predicted from trajectory information at sector entry position. In terms of operational validation, the proposed approach is envisioned as ATCO assisting tool, not an autonomous tool. Thus, there is always ATCO discretion element, and as more ATCO actions are collected, the models can be further trained for better accuracy. For future work, we will consider expanding the feature set by including parameters such as weather and wind. Moreover, human in the loop simulation will be performed to measure the effectiveness of the proposed approach.

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Intelligent air traffic controller simulation using artificial neural networks

Cited by 8 publications

References 5 publications

Optimization of a Mission-Based Flight Priority System

Optimization of a Mission-Based Flight Priority System

Review on artificial intelligence techniques for improving representative air traffic management capability

An Air Traffic Controller Action Extraction-Prediction Model Using Machine Learning Approach

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