Network congestion - Challenges of Growth Annex 4
In the 2018 iteration of the Challenges of Growth (CG18) study we took the opportunity of updated airport capacity plans and the new long-term traffic forecast to look again at the network behaviour in 2040.
According to the forecast, by 2040 traffic in Europe is expected to grow over 16.2 million flights in the Regulation and Growth scenario (most-likely), 53% more than the 2017 volume. Higher growth is expected in the Global Growth scenario, with around 20 million flights.
This growth in traffic will create pressure on airport capacity and will certainly reduce the number of slots available to act as contingency. When we analysed August and September 2016, there were just 6 airports that were “congested” in the sense of operating at 80% or more of their capacity for more than 6 consecutive hours per day. In the most-likely scenario of the 2040 forecast, this climbed to 16 airports in 2040. That is a small improvement on the 20 congested airports for the same conditions in the most-likely scenario from CG13, since the capacity growth between now and 2040 is now better targeted at the larger airports.
The observation of the airport capacity usage along the day gives us an overview of the global state of the network in 2040. The current 16% planned capacity growth by 111 airports (28% for the top 20 airports) is still not enough to manage the extra demand. By 2040, the top 20 airports will operate close or above 80% of their capacity starting with the first rotations till the end of the day in the most-likely scenario Regulation and Growth. With this future level of congestion, it becomes difficult to accommodate minor deviations from plan, and delays begin to accumulate rapidly.
For this iteration of Challenges of Growth we took the opportunity to update our delay model, originally focused on flow management (ATFCM) delays and nearer-term capacity planning, and which simulates the algorithm used by the Network Manager to respond to constraints. The key changes were to better simulate the distribution of non-ATM delay occurrences along a day of operation using EUROCONTROL/CODA statistics for the summer 2016. We used detailed data on actual turn-around times at airports to model how reactionary delays propagate from flight to flight during the day and we have been able to evaluate the level of flight cancellations that might be expected in response to strong delays. The analysis is based on modelling and comparing two summer months in the 2016 baseline year, and in 2040.