Learning Air Traffic Controller Workload from Past Sector Operations

Abstract : In this paper, we compare several machine learning methods on the problem of learning a model of the air traffic controller workload from historical data. This data is a collection of workload mesurements extracted from past sector operations and of ATC complexity measurements computed from radar records and airspace data (sector geometry). We assume that the workload is low when a given sector is collapsed with other sectors into a larger sector, normal when it is operated as is, and high when it is split into smaller sectors assigned to several working positions. This learning problem is modeled as a classification problem where the target variable is a workload category (low, normal, high) and the explanatory variables are the air traffic control (ATC) complexity metrics. Several classifiers are compared on this problem: linear dis-criminant analysis, quadratic discriminant analysis, naive Bayes classifiers, neural networks, and gradient boosted trees. The performance of these models is assessed on a separate test set. The best methods show a rate of correct predictions around 82%.
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Communication dans un congrès
ATM Seminar, 12th USA/Europe Air Traffic Management R&D Seminar, Jun 2017, Seattle, United States
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David Gianazza. Learning Air Traffic Controller Workload from Past Sector Operations. ATM Seminar, 12th USA/Europe Air Traffic Management R&D Seminar, Jun 2017, Seattle, United States. 〈hal-01592233〉

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