Brief description: 

Growth in the aviation transport domain has seen a constant increase between 5% and 6% annually over the last decade. Due to cost pressure and environmental constraints, rising traffic figures cannot be accommodated by expanding assets and extending existing infrastructure alone. There is a need for efficiency increase in operations to cope with the high demand. Data analytics is seen as a key enabler to unleash the inherent optimization potential. 

The use of integrated information will allow freeing up significant savings potentials. This is mainly achieved through two dogmas:

1)         Pro-active disruption management by analysis of historical data and comparison with actual real time data. This allows the prediction of unfavourable situations and the early deployment of counter-measures to avoid them.

2)         Optimizing operations across all involved domains through processing available data according to business goals.

By means of the use of big data, we are transferring these two dogmas to the real world to gain benefits in terms of operational efficiency and saving costs.

To tackle the airport planning times prediction  problem a combination of historic and real time data are used to  develop predictive model. A machine learning algorithm to detect clusters is used on the basis of recorded data of landing times, boarding times, taxi times connected to real time data feeds.

Main Features: 

Significant gains in operational efficiency will be achieved by turning available data into Integrated Intelligent Information. This is realized through analysis, integration, networking and implementation of smart applications and services across domains, to optimize operations on a holistic level. Instead of optimizing single stovepiped services, smart algorithms optimize processes according to selected criteria: best economy, best performance, maximum safety/security and maximum availability. Industry can define, prioritize and realize performance criteria according to their business goals. At the same time, data can provide insights to help to answer key business and process questions. Data leads to insights and they can be turned into decisions and actions that improve operations and business, achieving a complete data driven decision making.

Historical and real time data are combined to generate accurate predictions. These predictions are fed into a shadow system behind the operational system at the airport. The result is a more precise planning allowing more efficient use of existing resources and infrastructure as shown in the figure below:

The process of creating a prediction model consists of the following steps:

  • Analyse big data sets of historic recorded data
  • Correlate real time data feeds with the clusters detected
  • Build prediction model
  • fFeed model with data
  • Use more precise predictions for key planning times
Areas of Application: 
  • Airports
  • Airlines
  • Ground Handlers
Market Trends and Opportunities: 
  • Worldwide application
  • Used especially for big hubs and big airlines
  • The cost savings are obtained through efficiency gains
Customer Benefits: 

The ambition of the Replication Pilot is to optimize the aircraft turnaround time by improving the predictability of the processes analyzed and increasing the situational awareness of all actors involved in those processes. To achieve the target, a set of objectives have been defined (see D8.2 “Smart Airport Turnaround Performance Assessment Plan”) considering:

  • SEA’s clients’ needs regarding the current situation of passengers and flight flows through the Airport chosen as a Replication Pilot (i.e.: Malpensa), and
  • that a departing flight is fundamentally a continuation and reidentification of an arrival flight that transitions through a ‘ground trajectory’ phase

These objectives have been clustered in different tasks, summarized here below in working blocks. The achieved benefits are:

  • Getting more capacity with existing infrastructure
  • Less missed flights
  • Better adapted asset usage and steering
Technological novelty: 
  • has not been used in this context before
Component / Service / App


Jeppesen, BR&TE and SEA
BDV Reference categories: 
Data Analytics
Data Visualisation and Interaction
Transport, storage and logistics
Readiness Level: