D6.1 Aviation Use Case Detailed Definition

Authors: Cordero Jose Manuel, Costas Pablo, Fresno Cambre Laura, Lopez Leones Javier, Rossello Oliver Sebastian, Scarlatti David
Title: D6.1 Aviation Use Case Detailed Definition

This is the first deliverable (D6.1) of datAcron work package 6 “Aviation Use Case” which main objective is the validation of datAcron research results in an Aviation Industry use case. D6.1 “Aviation use case detailed definition” is devoted to setup a clear understanding for all datAcron partners about concepts and terminology, business objectives, data involved, events to detect and metrics to obtain.

The first part of the document presents Airlines and Air Traffic Management (ATM) business context. It establish the relation of Airline Revenue with flying “optimal” trajectories, i.e. minimizing main costs: fuel, time/schedule adherence related cost and air traffic services fees. Then it presents the Flight Plan as the “optimal” trajectory (agreed at a given time). Then the relation of Air Navigation Service Providers (ANSP) resource optimization with system uncertainty is explained and how the limits of automation are due to the same uncertainty. This leads to the objective of the use case: Increase predictability (=reducing uncertainty). Big Data approach applied to trajectory prediction can help to this improvement, understanding Big Data as Data-Driven (i.e. learning from historical data).

Two mains scenarios are selected for the use case: Flow Management and Flight Planning. Both scenarios are split in increasingly complex smaller scenarios which are related to datAcron components and technical work packages so the coverage of the different datAcron developments can be easily mapped.

Flow Management scenarios main objective is to allow better planning of the demand and capacity balance which will lead to less delays.

Flight planning scenarios main objective is to enhance the trajectory prediction to avoid plans prone to great deviations the day of operations.

Both scenarios leverage the analysis of historic data related to:

  • Flight Plans
  • Context ATM data
  • Surveillance data
  • Weather data
  • Flow Management
Year: 2016
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