Runway Manager

During the past decades airports have been steadily confronted with the problem of scaling their infrastructure according to changes in demand. Building additional runways is costly, has a long lead time, and the physical footprint of the airport increases considerably, with all its negative environmental and logistical consequences. A better utilization of existing capacity by packing traffic more densely decouples the demand from the infrastructure and makes the airports more agile, resilient, and responsive to unforeseen demand changes. As a solution to this challenge, we have developed the Integrated Runway Sequence Manager (IRSM) which leverages advanced AI algorithms to automatically plan arrival and departure flows as integrated and optimised runway sequences based on real-time information. Validation of IRSM in operational environments have demonstrated that airports can improve the capacity and throughput of existing infrastructures and airlines can improve flight predictability and punctuality. By reducing flight time before landings, or unnecessary waiting time at taxiways before departures, IRSM will lead to reduced jet fuel consumption and thus to reduced GHG emissions. The technology is mature and has reached TRL7. In this project we will explore potential paths to bring IRSM into real-world use. We must then better understand the total addressable market, competitors, and the barriers relating to testing/validation/certification requirements that are relevant for IRSM. From this insight we will explore business cases for alternative products, with the goal to identify a viable business case.