This thesis has presented a few novel approaches to analyze the effects of various disruptions on the US ATN, based on publicly available data. Our approach can be applied to any type of transportation system, provided enough data and appropriate parameters of interest are available. It is also possible to scale-up or scale-down the network size according to our needs. The statistical network-based model was able to combine network metrics and statistical methods and therefore overcome limitations of conventional theoretical models. It demonstrated a quantitative method to determining impacted airports while also being able to differentiate the influential air- ports from the smaller ones. An added benefit to this methodology is that it is flexible and can narrow down the scope of the analysis based on input. This could prove useful to analyze select airlines as well, to support their individual operations and management, for example to modify flight sched- ules for more resilience to external influences. In addition to the nodal level analysis, the research also introduced a similar network-level metric that can detect disruptions in the operations of the US ATN. The limitation of these approaches lie in the distribution of the data they operate on. If the data is not normally distributed then the results have to be treated with prudence. The second part of this thesis developed a method to predict future delays in the US ATN based on temporal, weather, and network-level features, in addition to the congestion of the network. The methodology was able to predict the state of airports 6 hours in the future with high accuracy. It also conceived a new metric that was able to reduce the dimensionality of the training data by incorporating network parameters in addition to the delay information of all of the airports in the network under consideration. The limitation of this method also is the data that the model trains on. This is partly due to incorporating so many features at once. If one feature is missing from a single data point the whole data point will not be useful for training.
One of the other issues is the distribution of the data is biased towards not being delayed on a normal day just like the data from the resilience quantification. This can be overcome by balancing the data before training the model.
In the future, these metrics can serve as an additional tool in analyzing and predicting the behavior of the network. The addition of spatial information to the analysis may also provide additional insights into operations. It is also possible to consider origin-destination pairs instead of just destination airports, the trade-off being omission of a large set of data that could be relevant. This method can also be used to create training data for developing impact prediction models. The machine learning model can be trained to predict the behavior of the network on the order of days in the future instead of hours.
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