Modeling and Integrating Flight-related Data for the Improvement of Airport Operations

Abstract

In this last decade, air transport has become one of the most popular means of transport among the population, due to the reduction in the price and the improvement of airport infrastructures. As a consecuence, the number of daily flights grows in an incessant way. Currently, there are more than 100,000 flights every day that need to be controlled and managed by air traffic management systems. This work examines how Big Data technologies can help in the development of new and more efficient air traffic control systems. The design of a Big Data architecture is proposed following a Data Lake design. This architecture is implemented using the Hadoop distributed processing framework, that will allow us to transform large volumes of data into useful information for air traffic management. We use air navigation surveillance data from multiple providers to obtain a wide coverage that allows us to know the trajectory that aircraft have made since takeoff to landing. These surveillance data are enriched with air control data to generate information of greater value. An evaluation of the generated data is made and a comparison that allows us to affirm that, the use of multiple surveillance information providers allows reconstructing more complete flown trajectories. Therefore, the trajectories obtained through the use of multiple providers will provide more information to the air control systems.