When planning airport operations, investments both air- and land side or only making next years budget you need to make some forecasts of what traffic you can expect. Now, there are many ways of doing that most of them ending up with a single figure for the monthly or weekly traffic. However we do know that the probability for that figure to be correct is near zero, thus we end up with plans based on assumptions that most likely newer will happen.
This is why we use Monte Carlo simulation to get a grasp of the uncertainty in our forecast and how this uncertainty develops as we go into the future. The following graph (from real life) shows how the passenger distribution changes as we go from year 2010 (blue) to 2017 (red). The distribution moves outwards showing an expected increase in Pax at the same time it spreads out on the x-axis (Pax) giving a good picture of the increased uncertainty we face.
This can also be seen from the yearly cumulative probability distributions given below. As we move out into the future the distributions are leaning more and more to the right while still being “anchored” on the left to approximately the same place – showing increased uncertainty in the future Pax forecasts. However our confidence in that the airport will reach at least 40M Pax during the next 5 years is bolstered.
If we look at the fan-chart for the Pax forecasts below, the limits of the dark blue region give the lower (25%) and upper (75%) quartiles for the yearly Pax distributions i.e. the region where we expect with 50% probability the actual Pax figures to fall.
The lower und upper limits give the 5% and 95% percentiles for the yearly Pax distributions i.e. we can expect with 90% probability that the actual Pax figures will fall somewhere inside these three regions.
As shown the uncertainty about the future yearly Pax figures is quite high. With this as the backcloth for airport planning it is evident that the stochastic nature of the Pax forecasts has to be taken into account when investment decisions (under uncertainty) are to be made. (ref) Since the airport value will relay heavily on these forecasts it is also evident that this value will be stochastic and that methods from decision making under uncertainty have to be used for possible M&R.
Major Airport Operation Disruptions
Delays – the time lapse which occurs when a planned event does not happen at the planned time – are pretty common at most airports Eurocontrol estimates it on average to approx 13 min on departure for 45% of the flights and approx 12 min for arrivals in 42% of the flights (Guest, 2007). Nevertheless the airport costs of such delays are small; it can even give an increase in revenue (Cook, Tanner, & Anderson, 2004).
We have lately in Europe experienced major disruptions in airport operations thru closing of airspace due to volcanic ash. Closed airspace give a direct effect on airport revenue and a higher effect the closer it is to an airport. Volcanic eruptions in some regions might be considered as Black Swan events to an airport, but there are a large number of volcanoes that might cause closing of airspace for shorter or longer time. The Smithsonian Global Volcanism Program lists more than 540 volcanoes with previous documented eruption.
As there is little data for events like this it is difficult to include the probable effects of closed airspace due to volcanic eruptions in the simulation. However, the data includes effects of the 9/11 terrorist attack and the left tails of the yearly Pax distributions will be influenced by this.
References
Guest, Tim. (2007, September). A Matter of time: air traffic delay in Europe. , EUROCONTROL Trends in Air Traffic I, 2.
Cook, A., Tanner, G., & Anderson, S. (2004). Evaluating the true cost to airlines of one minute of airborne or ground delay: final report. [University of Westminster]. Retrieved from, www.eurocontrol.int/prc/gallery/content/public/Docs/cost_of_delay.pdf
