Client’s Challenge
You have packed your suitcases, got up early to go to the airport to catch your flight, went through security smoothly, and now you are waiting for your flight to depart. Twenty more minutes, ten more minutes, and suddenly… Your flight is delayed. A not-so-great start to kick off your relaxing holiday. Unfortunately, it is a reality that around 18% of flights fail to depart on time and as a passenger, you often have no idea why your flight has been delayed.
So it may come as a surprise that frequently, flights are delayed or even canceled because of flight regulations. These regulations are coordinated by local authorities and the EUROCONTROL Network Manager Operations Centre (NMOC). A limited amount of flights (or even none) are allowed to land or take off for a certain period when these are issued. The local authorities and NMOC can issue these regulations due to multiple factors: bad weather, industrial actions, air traffic controller shortages, limited aerodrome capacity, and so on. These factors can cause the airport resources such as the runway, parking positions, terminal, and so on, to get too crowded, possibly exceeding the maximum capacity.
By issuing a regulation, airports reduce the risk of accidents, as air traffic controllers cannot adequately monitor the airspace when maximum capacity is exceeded. Sure, delays are bothersome, but safety always goes first. What EUROCONTROL wanted to do was predict these regulations and anticipate them. As this can be modeled as a time series problem, a machine learning solution was the perfect fit.
Our Solution
The assignment was clear: build a model that can predict regulations for the top 100 biggest European airports. However, there were several big challenges to overcome. Due to the many different variables, we had to select the most relevant data sources and transform and feed them to the AI model. While obtaining data might seem straightforward, it usually is not, as we have to start from unstructured data.
So, we started the project by developing a model for one airport with a clear proof-of-concept: predict regulations that weather conditions may cause. Flight regulations due to weather are most predictable: for example, when the weather is terrible, chances are that visibility is limited. First, we used historical weather data. Then, we added historical data regarding the number of flights. As we continued, we gradually added steps: we changed the historical data for prediction data. Think of the number of flights, notifications of work (think of instances where there is a crane in the vicinity of the runway), and ultimately, we integrated the four most prominent causes for regulations successfully:
Of course, there are other causes for flight regulations, such as incidents or accidents, but these are either unpredictable or do not happen often enough to incorporate into the model. So, when these indicators could predict the regulations for our first airport, we gradually extended the model to the top 100 European airports.
Results
After working on this solution for a long time, it was time to put it to work. Of course, our model could predict the regulations, but we also wanted people to have a clear overview of when and where a regulation would be issued. So, we developed a Power BI dashboard that users can easily use to see the visualizations of the predictions.
This solution fits right into our human-in-the-loop philosophy. Just because we can accurately predict regulations now does not mean that the problem has magically disappeared. The prediction platform, however, is a tool for people to see faster when such regulations will occur and therefore anticipate where the potential bottlenecks and problems in the network will emerge or even take preventive measures to avoid them.
Of course, now we keep looking for ways to improve the model. Moreover, we are looking into predicting how long flights will be delayed as well. Because what’s worse than a delay? Not knowing how long it will be. This way, hopefully, your next flight delay to your relaxing holiday is negligible.
