Research on Safety Comprehensive Assessment Method for Air Traffic Control Based on RBF Neural Network

2014 ◽  
Vol 989-994 ◽  
pp. 2671-2674
Author(s):  
Xu Sheng Gan ◽  
Hua Ping Li
Keyword(s):  
Neural Network ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Safety Assessment ◽  
Rbf Neural Network ◽  
Assessment Method ◽  
Air Traffic ◽  
Comprehensive Assessment ◽  
Assessment Index ◽  
Assessment Index System

To assess the safety situation in air traffic control effectively, a comprehensive assessment method based on RBF neural network is proposed. At first, the safety assessment principle based on RBF neural network is introduced, and then the safety assessment index system for air traffic control is established from four aspects of human, machine, environment and management. Simulation example of ATC safety assessment gives us a satisfactory result for RBF neural network.

Longitudinal Collision Risk Assessment of Paired Departure to Closed Spaced Parallel Runways

2014 ◽  
Vol 1030-1032 ◽  
pp. 2028-2033
Author(s):  
Zhao Ning Zhang ◽  
Hui Qiao ◽  
Ting Ting Lu
Keyword(s):  
Risk Assessment ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Safety Assessment ◽  
Error Distribution ◽  
Air Traffic ◽  
Assessment Model ◽  
Collision Risk ◽  
Safety Operation ◽  
Calculation Models

Paired departure to closed spaced parallel runways can effectively improve capacity of terminal, and also can solve congestion of busy airport, but it also increases the complexity of air traffic control .For ensuring safety operation of paired departure, the longitudinal collision risk of paired departure to closed spaced parallel runways was studied. Based on the acceleration error distribution and requirements on wake avoidance during paired departure, a longitudinal collision risk safety assessment model of closed spaced parallel runways paired departure was built. The parameters in this model were determined by providing the calculation models. In the end, an example was calculated to verify the model, and it turns out that this model is feasible.

Study on the Discrimination Method of Driving Safety States Based on BP Neural Network

2014 ◽  
Vol 556-562 ◽  
pp. 6111-6114
Author(s):  
Feng Ping Cao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Safety Assessment ◽  
Driving Safety ◽  
Assessment Index ◽  
Evaluation Indexes ◽  
The Neural Network ◽  
Discrimination Model ◽  
Assessment Index System ◽  
Discrimination Method

In order to estimating the state of driving safety and reducing accidents, a discrimination method of driving safety states based on BP neural network was presented in the paper. Firstly, the influencing factors on the vehicle driving safety were analyzed, and ten main factors that affected the driving safety of vehicles were confirmed, which constitute the safety assessment index system for vehicle driving. Then the discrimination model of driving safety states based on BP neural network was established, and inputs and outputs for the neurons were determined. At last, the input data for neurons were acquired on the basic of the main evaluation indexes of vehicle driving safety, and these data were used to train the neural network. The training result conform to expectations of the training requires.

Comparison of SAGAT and SPAM for Seeking Effective Way to Evaluate Situation Awareness and Workload During Air Traffic Control Task

2020 ◽  
Vol 64 (1) ◽  
pp. 1836-1840
Author(s):  
Mitsuki Fujino ◽  
Jieun Lee ◽  
Toshiaki Hirano ◽  
Yuichi Saito ◽  
Makoto Itoh
Keyword(s):  
Situation Awareness ◽  
Traffic Control ◽  
Traffic Management ◽  
Air Traffic Control ◽  
Air Traffic Management ◽  
Assessment Method ◽  
Air Traffic ◽  
Traffic Density ◽  
Assessment Technique ◽  
Nasa Tlx

Evaluation of air traffic controller’s situation awareness (SA) is becoming important for air traffic management with the growth of air traffic. This study compared two SA evaluation methods: Situation Awareness Global Assessment Technique (SAGAT) and Situation Present Assessment Method (SPAM) to understand how these techniques affect controllers’ predictability in different traffic density settings. Twenty students undertook simple air traffic control simulations by using both techniques. We investigated how these techniques affect their workload with Subjective Workload Assessment Technique (SWAT) and NASA-TLX. SWAT scores showed that high traffic density increased participants’ workload, and extra workload was posed right after answering SA queries. NASA-TLX scores were larger when SAGAT was used than when SPAM was used throughout the simulation. We found that the workload with SAGAT interferes with main tasks more than that of SPAM. The results of query scores suggested that SPAM is more predictive to the assessment of the controller’s SA.

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.

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