scholarly journals Probabilistic Prediction of Unsafe Event in Air Traffic Control Department Based on the Improved Backpropagation Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yong Liao ◽  
Zhiyang Miao ◽  
Changqi Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Safety Management ◽  
Mapping Method ◽  
Air Traffic ◽  
Occurrence Rate ◽  
Sigmoid Function ◽  
Control Department

Air traffic control is an important tool to ensure the safety of civil aviation. For the departments that do the work of air traffic control, reducing the percentage of unsafe event is the core task of safety management. If the relationship between the percentage of unsafe event and their influencing factors can be effectively clarified, then the probability of unsafe event in some control department can be predicted. So, it is of great importance to improve the level of safety management. To quantitatively estimate the probability of unsafe event, a three-layer BP neural network model is introduced in this paper. First, a probabilistic representation of unsafe event related to air traffic control department is made, and then, the probability of different classes of unsafe events and safe events is taken as the outputs of the BP neural network, the factors influencing occurrence of unsafe event connected with air traffic control is taken as inputs, and the sigmoid function is chosen as activation function for the hidden layer. Based on the error function of neural network, it is proved that the general BP neural network has two drawbacks when used for the training of small probability events, which are as follows: the pattern does not ensure that the sum of probability of all events is equal to one and the relative error between the actual outputs and desired outputs is very large after the training of neural network. The reason proved in this paper is that the occurrence rate of the unsafe event is much smaller than that of the safe event, resulting in each weight in the hide layer being subjected to the desired outputs of the safe event when using the gradient descent method for network training. To address this issue, a new mapping method is put forward to reduce the large difference of the desired outputs between the safe event and unsafe event. It is theoretically proved that the mapping method proposed in this paper can not only improve the training accuracy but also ensure that the sum of probability is equal to one. Finally, a numeric example is given to demonstrate that the method proposed in this paper is effective and feasible.

scholarly journals A Genetic Algorithm-based BP Neural Network Method for Operational Performance Assessment of ATC Sector

2016 ◽  
Vol 28 (6) ◽  
pp. 563-574 ◽  
Author(s):  
Jianping Zhang ◽  
Liwei Duan ◽  
Jing Guo ◽  
Weidong Liu ◽  
Xiaojia Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Performance Assessment ◽  
Bp Neural Network ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Back Propagation ◽  
Air Traffic ◽  
Operational Performance ◽  
Network Method

To assess operational performance of air traffic control sector, a multivariate detection index system consisting of 5 variables and 17 indicators is presented, which includes operational trafficability, operational complexity, operational safety, operational efficiency, and air traffic controller workload. An improved comprehensive evaluation method, is designed for the assessment by optimizing initial weights and thresholds of back propagation (BP) neural network using genetic algorithm. By empirical study conducted in one air traffic control sector, 400 sets of sample data are selected and divided into 350 sets for network training and 50 sets for network testing, and the architecture of genetic algorithm-based back propagation (GABP) neural network is established as a three-layer network with 17 nodes in input layer, 5 nodes in hidden layers, and 1 node in output layer. Further testing with both GABP and traditional BP neural network reveals that GABP neural network performs betterthan BP neural work in terms of mean error, mean square error and error probability, indicating that GABP neural network can assess operational performance of air traffic control sector with high accuracy and stable generalization ability. The multivariate detection index system and GABP neural network method in this paper can provide comprehensive, accurate, reliable and practical operational performance assessment of air traffic control sector, which enable the frontline of air traffic service provider to detect and evaluate operational performance of air traffic control sector in real time, and trigger an alarm when necessary.

Aeronautical Study for Flight Time Estimation Applying Artificial Neural Network and Genetic Algorithm

2014 ◽  
Vol 919-921 ◽  
pp. 1063-1074
Author(s):  
Yung Ching Lin ◽  
Lee Kuo Lin ◽  
Shao Hong Tsai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Flight Time ◽  
Air Traffic ◽  
Artificial Neural

Since the adoption of open-air policy, people make more frequent use of air travel to do various business or tourism activities. The volume of air traffic has greatly increased, along with the occurrences of traffic jam in the air. Delays of landings or take-offs and the congestions in the approach air space have become commonplace, exacerbating the already heavy workload of air-traffic controllers and the inadequacies of ATC system. Therefore, a study of flight time in ATC operation to help alleviate airspace congestions has become more and more urgent and important. Taking international airway A1 as an example, this study makes use of the known entry time, flight altitude, speed, penetrating and descending as the input of artificial neural networks; the time between departure and transfer point as the output of Artificial Neural Networks, to establish artificial neural network. Applying artificial neural networks and genetic algorithm to the study to simulate the result of actual flight, one can precisely estimate the flight time, thereby making it an efficient air-traffic-control instrument. It can help controllers handle different time segments of air traffic, thus upgrading the quality of air traffic control service.

Artificial Intelligence for Evaluating the Mental Workload of Air Traffic Controllers

2020 ◽  
pp. 184-212
Author(s):  
Tetiana Shmelova ◽  
Yuliya Sikirda
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Expert System ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Mental Workload ◽  
Air Traffic ◽  
Control Center ◽  
Air Traffic Controllers ◽  
And Control

In this chapter, the authors propose the application of artificial intelligence (namely expert system and neural network) for estimating the mental workload of air traffic controllers while working at different control centers (sectors): terminal control center, approach control center, area control center. At each air traffic control center, air traffic controllers will perform the following procedures: coordination between units, aircraft transit, climbing, and descending. So with the help of the artificial intelligence (AI) and its branches expert system and neural network, it is possible to estimate the mental workload of dispatchers for a different number of aircraft, compare the workload intensity of the air traffic control sectors, and optimize the workload between sectors and control centers. The differentiating factor of an AI system from a standard software system is the characteristic ability to learn, improve, and predict. Real dispatchers, students, graduate students, and teachers of the National Aviation University took part in these researches.

scholarly journals Safety Management System in Croatia Control Ltd.

2012 ◽  
Vol 21 (4) ◽  
pp. 279-284
Author(s):  
Stanislav Pavlin ◽  
Vedran Sorić ◽  
Dragan Bilać ◽  
Igor Dimnik ◽  
Daniel Galić
Keyword(s):  
Traffic Control ◽  
Air Traffic Control ◽  
Management System ◽  
Safety Management ◽  
Air Traffic ◽  
Civil Aviation ◽  
Special Focus ◽  
International Civil Aviation Organization ◽  
Safety Management System ◽  
Navigation Service

International Civil Aviation Organization and other international aviation organizations regulate the safety in civil aviation. In the recent years the International Civil Aviation Organization has introduced the concept of the safety management system through several documents among which the most important is the 2006 Safety Management Manual. It treats the safety management system in all the segments of civil aviation, from carriers, aerodromes and air traffic control to design, construction and maintenance of aircraft, aerodromes, those who produce instruments, equipment and parts for the needs of civil aviation and others. This paper presents and partly deals with the documents from the safety management system domain and the system implementation in Croatia with special focus on the Croatia air navigation service provider, Croatia Control Ltd. KEY WORDS: safety management system, safety, air traffic control

Gray Correlation Analysis of Human Factors for Unsafe Events in Air Traffic Control

2012 ◽  
Vol 490-495 ◽  
pp. 1135-1139
Author(s):  
Wei Zhen Tang
Keyword(s):  
Human Factors ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Safety Management ◽  
Correlation Method ◽  
Air Traffic ◽  
Practical Significance ◽  
Communication Problems ◽  
Gray Correlation ◽  
Traffic Control System

This thesis demonstrates a systematical analysis of factors resulting in unsafe events in air traffic control with gray correlation method. According to the case study of one ATM Bureau from 2004 to 2008, most of the human factors are attributed to communication problems between pilots and controllers. Therefore, this research is of great practical significance in improving and perfecting the safety management of air traffic control system.

scholarly journals An analysis of wildlife strikes to aircraft in Brazil: 2011-2018

2020 ◽  
Vol 10 (2) ◽  
pp. 51
Author(s):  
Flavio A.C. Mendonca ◽  
Julius Keller ◽  
Chenyu Huang
Keyword(s):  
Traffic Control ◽  
Safety Management ◽  
Economic Problem ◽  
Air Traffic ◽  
Annual Reports ◽  
Economic Losses ◽  
Traffic Operations ◽  
Descriptive Data ◽  
Aircraft Accidents ◽  
Control Department

Purpose: Aircraft accidents due to wildlife hazards have become a growing safety and economic problem to the Brazilian and international aviation industries. These safety occurrences have resulted in significant direct and indirect economic losses as well injuries and fatalities worldwide. The purpose of this study was to develop empirical information obtained from the analysis of wildlife strike and aircraft operations data in Brazil that could be used for accident prevention efforts.Design/methodology: The research team collected and analyzed aircraft operations as well as wildlife strike data from the 32 busiest commercial airports in Brazil, from 2011 through 2018. Researchers obtained the number of aircraft operations at each of those 32 Brazilian airports from the Brazilian air traffic operations annual reports published by the Air Traffic Control Department. Wildlife strike data from the studied airports were obtained from the Brazilian national wildlife strike database. Descriptive data analysis was adopted to provide an intuitive and overall trend of wildlife strikes at and the 32 busiest commercial airports in Brazil.Findings: Results indicate that the number of wildlife strikes at and around the investigated airports increased 70% even though the number of aircraft operations at these airports declined by 12% during the period studied. Birds were involved in 88% of the reported events. Most reported strikes (59%) and damaging strikes (39%) occurred during the arrival phases-of-flight. Most (33%) strikes were reported by airport personnel. A finding of concern was that the majority of wildlife strikes (97%) and damaging wildlife strikes (96%) occurred within the airport environment.Originality/value: The current project contributes to the safety management of wildlife hazards in Brazil by conducting a comprehensive analysis of wildlife strike and aircraft operations data (2011-2018) in the 32 busiest Brazilian commercial airports. 

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