Data-driven delay estimation and anomaly detection: A study on European and Turkish air traffic

Abstract

Air traffic networks represent highly complex and interconnected physical systems. Unlike other transportation networks, air traffic is very heavily regulated and physically constrained. Although the airways and airspaces are somehow more flexible compared to land based transportation systems, the fact that aircrafts can only positioned on and operated by airports make them quite dependent on the operations of the airports. Air traffic is regulated to ensure safety, while also maintaining the throughput of travel from one location to another. While these regulations does a decent job on keeping the air travel safe and systematical, they fall short when there are disruptions among the network that hinders the air traffic. There are numerous reasons for disruptions in air transportation; weather conditions, accidents, capacity constraints, personnel strikes etc. Yet their negative effect to the air traffic is mostly the same: introducing delays. Due to the connected nature of the air traffic and airports, when a delay generating event occurs at one place, the other members of the network could experience the similar effects, if not at a larger degree. This delay propagation means there is a ripple effect through the network which can snowball the delay generations and cause very large congestions. To relieve the effects of delay generating events, air traffic federators regulate the air traffic in a reactionary way. This may include reducing the capacity on certain airports or airways, giving NOTAMs, holding aircrafts on the ground or in the air (with hold patterns). Since all these actions are \emph{reactionary}, they are set in place after the delays already propagates through the network since it is trivial to asses and quantify the propagations in a large and complex network system. This study hypothesis that if the air traffic network can be modeled so that the propagations can be accurately calculated, it becomes possible to take proactive actions instead of reactive ones. Proactive actions are significantly more important when there is a risk of snowballing and propagation. It allows to take action when the ill effects are still contained on fewer members with smaller intensities. This paves the way for a more effective and less costly approach. Hence, the study proposes a method with 3 main parts; first one is to model the air traffic network so that propagations can be quantified, second one is to estimate the parameters of this model to keep a short-sighted vision into the upcoming network state and third one is to come up with a comprehensive action generating model to find optimal proactive actions that can keep the delay spreading at minimum and improve system resiliency. The air traffic modeling part is done via adopting compartmental model from epidemiology. This model explains the tranmission of disease within a population. When it is applied to the physical network system, instead of disease and humans, the delay amount and aircrafts is used. Additionally with the meta population model, instead of considering aircrafts one by one, airports can be used as they are focused points of aircraft populations. By linking transmit rate to the flight frequency between airports and the recovery rate to the delay handling characteristics of the airport, The parameter estimation part is done by calculating the historic recovery rates of the airports and then using deep learning inference to predict the next time step's recovery rates. The other parameters of the air traffic model, such as the traffic flow, is already known before hand (flight plans). Therefore through the estimation of recovery rate the network state of the upcoming states can be accurately predicted. This prediction can then be fed to the action generating algorithm to make the most informed decision. The action generating algorithm therefore must fundamentally be a deterministic state to action mapper. Reinforcement learning approach is utilized to train this state to action mapper to make it capable of generating optimal decisions under a sufficiently large spectrum of conditions. The final part of this study concerns with anomalous flight detection in air traffic as these types of flights are one of the sources of disruptions in an air traffic network. Although flight paths naturally diverge from one another, they still adhere to a set of patterns that have been tested in various environments and are optimized for them. These patterns may or may not be simple, depending on a number of factors, such as airspace use, the cognitive complexity of controllers, the weather, and NOTAMs. It is a challenging task to accurately classify flights just by their trajectories into a desired set of categories based solely on its statistical properties because of the high variance. For this purpose, the study incorporates a statistical approach that takes into account the time-based characteristics of the flight trajectories to determine whether they are abnormal or not. This statistical method with LSTM autoencoders makes it possible to train the model with historical data and quickly predict the flight class, taking into account the time-based characteristics of a flight trajectory. LSTM autoencoders can capture the class of a flight with relatively shorter time windows (16 second intervals). Therefore the air space can be periodically sweeped for anomalies while the network model and action algorithm runs in parallel. The obtained results demonstrate that the suggested architecture is quite capable of classifying abnormal flight trajectories as it successfully detects simulated fighter aircraft trajectories in airspaces with high commercial flight density. With the applications of deep learning and reinforcement learning, this whole methodology ensembles is largely data-driven, however the introduction of the compartmental model from epidemiology lays out a strong and accurate mathematical formula to support these data-centric approach. As the results suggests, The whole network's resiliency, i.e. its ability to keep delays from spreading and absorbing them, significantly increases when the optimal actions are reflected on the parameters. Additionally with the help of unsupervised learning, anomalous flights are also detected and represented as a disruption source to the network. Possible biases and shortcomings due to the data-driven approach is recognized throughout the study yet the overall method is deemed to be of significant importance in terms of managing resiliency through air traffic networks.