scholarly journals Neural Network-Based Aircraft Conflict Prediction in Final Approach Maneuvers

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1708
Author(s):  
Rafael Casado ◽  
Aurelio Bermúdez
Keyword(s):  
Neural Network ◽  
Traffic Management ◽  
Autonomous Navigation ◽  
Decision Support Tool ◽  
Air Traffic ◽  
Navigation Systems ◽  
Support Tool ◽  
Final Approach ◽  
Aircraft Trajectories ◽  
Available Information

Conflict detection and resolution is one of the main topics in air traffic management. Traditional approaches to this problem use all the available information to predict future aircraft trajectories. In this work, we propose the use of a neural network to determine whether a particular configuration of aircraft in the final approach phase will break the minimum separation requirements established by aviation rules. To achieve this, the network must be effectively trained with a large enough database, in which configurations are labeled as leading to conflict or not. We detail the way in which this training database has been obtained and the subsequent neural network design and training process. Results show that a simple network can provide a high accuracy, and therefore, we consider that it may be the basis of a useful decision support tool for both air traffic controllers and airborne autonomous navigation systems.

scholarly journals Identifying Operational Benefits of the Arrival Management System – A KPI-Based Experimental Method by Evaluating Radar Trajectories

2021 ◽  
Vol 33 (5) ◽  
pp. 633-645
Author(s):  
Songwei Liu ◽  
Junfeng Zhang ◽  
Zihan Peng ◽  
Haipeng Guo ◽  
Anle Pi
Keyword(s):  
Traffic Management ◽  
Decision Support Tool ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Support Tool ◽  
Performance Improvements ◽  
System A ◽  
Original Intention ◽  
Air Traffic Controllers

The arrival management (AMAN) system is a decision support tool for air traffic controllers to establish and maintain the landing sequence for arrival aircraft. The original intention of designing the AMAN system is to improve the efficiency of air traffic management (ATM), but few studies are investigating the operational benefits of this system based on key performance indicators (KPIs) and evaluating actual data in a real-time environment. The main purpose of this paper is to propose a KPI based transferable comparative analysis method for identifying the operational benefits of the AMAN through radar trajectories. Firstly, six KPIs are established from a joint study of the mainstream ATM performance frameworks worldwide. Secondly, appropriate evaluation technique approaches are determined according to the characteristics of each KPI. Finally, a Chinese metropolitan airport is taken for the case study, and three periods are defined to form data samples with high similarity for comparative experiments. The results validate the feasibility of the proposed method and find comprehensive performance improvements in arrival operations under the effects of the AMAN system.

Neural Network Ensemble Approach to Pushed Convoys Dispatching Problems

2020 ◽  
Vol 27 (1) ◽  
pp. 70-82 ◽  
Author(s):  
Aleksandar Radonjić ◽  
Danijela Pjevčević ◽  
Vladislav Maraš
Keyword(s):  
Neural Network ◽  
Decision Support Tool ◽  
Primary Objective ◽  
Danube River ◽  
Decision Making Process ◽  
Neural Network Ensemble ◽  
Support Tool ◽  
Shipping Company ◽  
The Republic ◽  
Power Measurements

AbstractThis paper investigates the use of neural networks (NNs) for the problem of assigning push boats to barge convoys in inland waterway transportation (IWT). Push boat–barge convoy assignmentsare part of the daily decision-making process done by dispatchers in IWT companiesforwhich a decision support tool does not exist. The aim of this paper is to develop a Neural Network Ensemble (NNE) model that will be able to assist in push boat–barge convoy assignments based on the push boat power.The primary objective of this paper is to derive an NNE model for calculation of push boat Shaft Powers (SHPs) by using less than 100% of the experimental data available. The NNE model is applied to a real-world case of more than one shipping company from the Republic of Serbia, which is encountered on the Danube River. The solution obtained from the NNE model is compared toreal-world full-scale speed/power measurements carried out on Serbian push boats, as well as with the results obtained from the previous NNE model. It is found that the model is highly accurate, with scope for further improvements.

Analysis of Air Traffic Control Command Entries and the Impact on Decision Support Tool Performance

2014 ◽  
Vol 22 (1) ◽  
pp. 1-20
Author(s):  
Jasenka Rakas ◽  
Michael Seelhorst ◽  
Bona Bernard Niu ◽  
Jeffrey Tom ◽  
Confesor Santiago
Keyword(s):  
Decision Support ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Decision Support Tool ◽  
Air Traffic ◽  
Support Tool ◽  
Tool Performance ◽  
Control Command ◽  
The Impact

Combining Process Tracing and Policy Capturing Techniques for Judgment Analysis in an Anti-Submarine Warfare Simulation

2021 ◽  
Vol 65 (1) ◽  
pp. 1557-1561
Author(s):  
Katherine Labonté ◽  
Daniel Lafond ◽  
Bénédicte Chatelais ◽  
Aren Hunter ◽  
Folakemi Akpan ◽  
...  
Keyword(s):  
Decision Support Tool ◽  
Machine Learning Algorithms ◽  
Process Tracing ◽  
Policy Capturing ◽  
Support Tool ◽  
Human Decision ◽  
Model Training ◽  
Decision Policies ◽  
Available Information

The Cognitive Shadow is a prototype decision support tool that can notify users when they deviate from their usual judgment pattern. Expert decision policies are learned automatically online while performing one’s task using a combination of machine learning algorithms. This study investigated whether combining this system with the use of a process tracing technique could improve its ability to model human decision policies. Participants played the role of anti-submarine warfare commanders and rated the likelihood of detecting a submarine in different ocean areas based on their environmental characteristics. In the process tracing condition, participants were asked to reveal only the information deemed necessary, and only that information was sent to the system for model training. In the control condition, all the available information was sent to the system with each decision. Results showed that process tracing data improved the model’s ability to predict human decisions compared to the control condition.

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