Classification of air traffic control scenarios using decision trees: insights from a field study in terminal approach radar environment
| Autor: | Christina Malaki, Panagiotis Psaros, Tom Kontogiannis, Stathis Malakis |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Decision trees Decision tree Stability (learning theory) Poison control ComputerApplications_COMPUTERSINOTHERSYSTEMS Context (language use) Classification rules Machine learning computer.software_genre 050105 experimental psychology Air traffic control 0501 psychology and cognitive sciences 050107 human factors Interpretability Air traffic flow management business.industry 05 social sciences Complexity Capacity management Computer Science Applications Human-Computer Interaction Philosophy Systems design Artificial intelligence business computer |
| Zdroj: | Cognition, Technology & Work. 22:159-179 |
| ISSN: | 1435-5566 1435-5558 |
| Popis: | Summarization: Air traffic controllers are responsible for the safe, expeditious and orderly flow of the air traffic. Their training relies heavily on the use of simulators that can represent various normal and emergency situations. Accurate classification of air traffic scenarios can provide assistance towards a better understanding of how controllers respond to the complexity of a traffic scenario. To this end, we conducted a field study using qualified air traffic controllers, who participated in simulator sessions of terminal radar approach control in a variety of scenarios. The aim of the study was twofold, firstly to explore how decision trees and classification rules can be used for realistic classification of air traffic scenarios and secondly to explore which factors reflect better operational complexity. We applied machine learning methods to the data and developed decision trees and classification rules for these scenarios. Results indicated that decision trees and classification rules are useful tools in accurately categorizing scenarios and that complexity requires a larger set of predictors beyond simple aircraft counts. The derived decision trees and classification rules performed well in prediction, stability and interpretability. Practical benefits can be derived in the areas of operations and system design in the context of air traffic flow and capacity management systems. Presented on: Cognition, Technology and Work |
| Databáze: | OpenAIRE |
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