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TRANSPORT-Transport 2025

Develop Airport Logistics Intelligence (ALI) , a system for optimizing flow of baggage using AI and prediction.

Alternative title: Utvikle Airport Logistic Intelligence (ALI), et system for å optimalisere bagasjeflyt ved å bruke AI og prediksjon.

Awarded: NOK 6.0 mill.

Project Manager:

Project Number:


Project Period:

2019 - 2024

Funding received from:



The aircraft industry is rapidly changing and as a consequence of new IATA guidelines, checked in luggage need to be scanned at a minimum of four points: at check-in, loading of the aircraft, transfers between the aircraft and on arrival. These mandated changes takes aim to reduce lost baggage and to increase security. This project intent to give our customers tools that enables them to fully implement IATA resolution 753, and to increase their control over baggage flow and handling beyond these guidelines. Stakeholders such as airports and airlines are very positive to a system that enables efficient and fair routing of baggage, and reduces costs associated with delays, missing luggage and missed take-off/lading slots. Our system, Airport Logistics Intelligence (ALI), consists of 3 products: 1) Smart Flow is a system for following and mapping the flow of baggage throughout the Baggage Handling System (BHS). The system is capable of making decisions and prioritize based on artificial intelligence. Additionally, it uses a "multi-agent" approach that compartmentalizes parts of the BHS, and tasks these to monitor it's own performance, leading to savings in energy usage and increased efficiency. 2) A Big Data platform for prediction and forecasting. The main use of this product is for predicting failures, heavy volumes of baggage and staff requirements. 3) A Dashboard, that display the complex data in an intuitive way. This give the control staff real time statistics and visualizations. In addition, we plan to research the use of existing sensors, such as cameras for a more comprehensive data-feed for real-time information by using new capabilities of graphics cards. The project has planned 5 major steps: 1) research, modelling, sensors simulations 2) developing the platform and dashboard 3) develop and train the AI 4) develop test models for validating the research 5) testing and improvement

Air traffic is increasing rapidly. IATA has mandated resolution 753 that requires bags to be scanned at 4 points: check-in, aircraft loading, transfers between aircraft and on arrival. However, seen from a security perspective and the core purpose of BHS, it is also necessary to keep track of the bag through the whole flow in the BHS. By tracking bags through the whole Baggage Handling System, performing continuous analysis and prediction, operators of airports can predict heavy baggage flow, if the system requires maintenance or is appropriering breakdown states. To accomplish this, a number of classification and prediction algorithms needs to be developed and tuned, AI methods needs to be investigated and neural networks need to be developed and trained. To improve situational awareness a dashboard that provides operators with a real-time status and salient predictions, need to be developed and fine tuned to reduce information overload. The big data platform provides the basis for preemptive and predictive capabilities and it need to transform and merge data into various applied statistical time series analysis, neural networks and supervised/ reinforcement learning. Among the hardest of the challenges, is how to apply video-data for inventory and flow control and how to properly distribute and optimize Agent responsibilities in the Multi Agent System. Assuming a successful project, airport operators will be able to leverage existing data to decrease congestion, reduce cost of maintenance and breakdowns, energy use, personell costs and to more efficiently employ existing BHS eliminating needs for upgrades. Airlines will be able to better coordinate the flow of baggage to reduce delays on tight of delayed transfers in accordance to the principles of priority and fairness. They can also use the data platform to provide their customers with fee-based tracking of baggage.

Funding scheme:

TRANSPORT-Transport 2025