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Item type:Publication, Access status: without files , An exploratory research on European ATM Network resilience through supervised learningPublicationtype: Doctoral Thesis16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Toward ATM resiliency : a deep CNN to predict number of delayed flights and ATFM delay(Multidisciplinary Digital Publishing Institute (MDPI), 2021-01-25); ; The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency.Publicationtype: Journal ArticleScopus© Citations 9 182 234 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A study of Capacity Regulations to define European Air Traffic Management Network States(2021); ; The European Air Traffic Management Network is unique in its complexity due to numerous stakeholders, airspace configurations and accommodated traffic volumes. Such a complexity challenges both tactical visibility and the network resiliency in general. Therefore we reviewed different perspectives of resiliency and robustness to explore advantages of performance variability in understanding network states. The methodology is based on capturing the emergent disruptions as drivers of performance variability. We claim that capacity regulations reveal restorative mechanisms for tactical planning. Consequently, we conducted statistical evaluations to provide quantitative measures for network state definitions. The methodology is even able to distinguish non-nominal disruptions to either crisis or critical states. The proposed approach is then demonstrated by a data sample covering six months. Furthermore to assess the severity of nonnominal states, we estimated probability distributions of different regulation types. The study is concluded by addressing long term network resiliency based on estimated probability distributions.Publicationtype: Journal Article58