Aurora-AI has developed multiple AIs that deliver highly accurate predictions to improve operational performance. These offer the real prospect of being able to develop a “Digital Twin” of an Airport to enable What-if? Scenario planning and real-time updates to improve intervention planning when operational plans are disturbed by events.
These techniques have also been applied to other Big Data challenges generating real business change.

Car Park Occupancy

Predict Number of Cars in the Car Park from Arrival Schedule

  • Aurora-AI used Aircraft Schedule and records of the number of passengers on each flight to build an accurate prediction of the occupancy levels of the Terminal Car Park in 4 hours time.
  • High degrees of correlation were achieved within days, far exceeding traditional machine learning techniques. This has been taken as a proof of concept and is now being applied to other prediction requirements as part of the development of a “Digital Twin” model of the Airport.

Customer Classification

Classify Customer Behaviour from Online Data

  • AIs have been produced that classify behaviours of people using specific websites. This classification is used to control responses in real-time.
  • Profit margins are improved and costs of the online assessment are significantly reduced over the traditional methods that have been used in the past.