Scipediacontent: Scipediacontent moved page Draft Content 535086418 to Gianazza 2017b

2021-02-03T20:16:31Z

Scipediacontent moved page Draft Content 535086418 to Gianazza 2017b

Scipediacontent: Created page with " == Abstract == International audience; In this paper, we compare several machine learning methods on the problem of learning a model of the air traffic controller workload f..."

2021-02-03T20:16:28Z

Created page with " == Abstract == International audience; In this paper, we compare several machine learning methods on the problem of learning a model of the air traffic controller workload f..."

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== Abstract ==

International audience; 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%.

== Original document ==

The different versions of the original document can be found in:

* [https://hal-enac.archives-ouvertes.fr/hal-01592233 https://hal-enac.archives-ouvertes.fr/hal-01592233],
: [https://hal-enac.archives-ouvertes.fr/hal-01592233/document https://hal-enac.archives-ouvertes.fr/hal-01592233/document],
: [https://hal-enac.archives-ouvertes.fr/hal-01592233/file/12th_ATM_RD_Seminar_paper_37.pdf https://hal-enac.archives-ouvertes.fr/hal-01592233/file/12th_ATM_RD_Seminar_paper_37.pdf]

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Gianazza 2017b - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 535086418 to Gianazza 2017b

Scipediacontent: Created page with " == Abstract == International audience; In this paper, we compare several machine learning methods on the problem of learning a model of the air traffic controller workload f..."