transportation growth is a reality described by different sources (e.g. The World Bank , the latest Eurocontrol report ). One essential initiative required to improve air traffic capacity while maintaining or increasing safety is to introduce predictive analytics that enable a dynamic adaptation of airline operations in a preemptive manner to an ever changing environment. An important part of this task is to model airport operations and plan accordingly. Particularly runway usage and/or configuration are important aspects of these operations. For example, prior knowledge of runway usage could improve flight plan optimizers outputs. Of course, to create any model or predictor, ground truth data is required. However most of the time, detailed information about runway historical usage/configuration is inaccessible, unreliable or it belongs to national ATC services providers. Then, thinking on a high-scale forecast methodology there is an important drawback given the lack of a feasible target for most of the airports. Thus, the goal of this work is to introduce an accessible, easy to implement algorithm that allows historical reconstruction of runway usage/configuration for any airport based on data transmitted from aircrafts through either Radar or ADS- B technologies, even when the track data is not consistent. We study the quality of the assessment performed by the two outputs of the algorithm: 1) Measuring runway usage accuracy in comparison to the report given by the Spanish ATC service provider (ENAIRE) for each flight landing to or taking off from two Spanish airports, Madrid-Barajas and Barcelona-El Prat, during October 2016. 2) Comparing the Netherlands-Schiphol runway configuration reported by the Netherlands airspace regulator (LVNL) for three different months: February, April and August 2018. The results provide values above 97% of accuracy for both types of assessment.
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