Forecasts passenger and baggage volumes and corresponding arrival curves at various touchpoints across the terminal. Provides an automated planning flow in real-time while machine learning ensures continuous data-driven improvement.
Allocates baggage make-up positions considering any early baggage storage and secures optimized use of infrastructure. Allocation of baggage reclaim belts provides balanced use of reclaim belts, thereby improving the passenger flow.
In development. It will allocate stands and gates, considering all constraints and forecasts estimated times and aircraft links using machine learning. Thanks to the Horizon 2020 funding, the solution will be taken to the next level, which includes refining the machine learning components and advancing Better Airport®’s aircraft allocation solution.
Since forming Copenhagen Optimization in 2014, we have been working with major airports across the globe delivering significant benefits. This includes a 51% reduction in security wait times at Geneva International Airport, an 18% efficiency gain in check-in and baggage at Toronto Pearson, and a 10.4% increase in security staff efficiency at Dublin Airport. Read all our cases below:
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