Abstract
International audience; In this paper, we assess the performance of a workload model trained on a subset of sectors, focusing on how it generalizes on fresh sectors. The model of the air traffic controller workload is learned from historical data made of workload mesurements extracted from past sector operations and ATC complexity measurements computed from radar records and airspace data (sector geometry). The workload is assumed to be 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. In previous work, we compared several classifiers on this problem. The models were trained on one week of traffic, and their generalization performance was assessed on another week of traffic, using the same sectors in both the training and test sets. In the current work, we examine if models learned on a specific set of sectors can be performant on any other sector, or not. We also give a closer look at how the workload varies with the ATC complexity measures in our data, using bagplots of the data points for a few sector instances. The results allow us to better understand the strengths and limits of our data-driven model.
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Published on 01/01/2017
Volume 2017, 2017
Licence: CC BY-NC-SA license
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