The aviation industry is of great importance for a globally connected economy. Customer satisfaction with airlines and airport performance is considerably influenced by how much flights are delayed. But how should the delay be quantified with thousands of flights for each airport and airline? Here, we present a statistical analysis of arrival delays at several UK airports between 2018 and 2020. We establish a procedure to compare both mean delay and extreme events among airlines and airports, identifying a power-law decay of large delays. Furthermore, we note drastic changes in plane delay statistics during the COVID-19 pandemic. Finally, we find that delays are described by a superposition of simple distributions, leading to a superstatistics.

No abstract

In modern societies, the rampant growth of the Internet, both on the technological and social level, has created fertile ground for the emergence of new types of risk. On top of that, it enhances pre-existing threats by offering new means for accessing and exploiting Critical Infrastructures. As the kinds of potential threats evolve, the security, safety and resilience of these infrastructures must be updated accordingly, both at a prevention, as well as a real-time confrontation level. Our research approaches the security of these infrastructures with a focus on the data and utilization of every possible piece of information that derives from this ecosystem. Such a task is quite daunting, since the quantity of data that requires processing resides in the Big Dataspace. To address this, we introduce a new well-defined Information Life Cycle in order to properly model and optimise the way information flows through a modern security system. This life cycle covers all the possible stages, starting from the collection phase up until the exploitation of information intelligence. That ensures the efficiency of data processing and filtering while increasing both the veracity and validity of the final outcome. In addition, an agile Framework is introduced that is optimised to take full advantage of the Information Life Cycle. As a result, it exploits the generated knowledge taking the correct sequence of actions that will successfully address possible threats. This Framework leverages every possible data source that could provide vital information to Critical Infrastructures by performing analysis and data fusion being able to cope with data variety and variability. At the same time, it orchestrates the pre-existing processes and resources of these infrastructures. Through rigorous testing, it was found that response time against hazards was dramatically decreased. As a result, this Framework is an ideal candidate for strengthening and shielding the infrastructures’ resilience while improving management of the resources used.

The aviation industry is of great importance for a globally connected economy. Customer satisfaction with airlines and airport performance is considerably influenced by how much flights are delayed. But how should the delay be quantified with thousands of flights for each airport and airline? Here, we present a statistical analysis of arrival delays at several UK airports between 2018 and 2020. We establish a procedure to compare both mean delay and extreme events among airlines and airports, identifying a power-law decay of large delays. Furthermore, we note drastic changes in plane delay statistics during the COVID-19 pandemic. Finally, we find that delays are described by a superposition of simple distributions, leading to a superstatistics.

The aviation industry is of great importance for a globally connected economy. Customer satisfaction with airlines and airport performance is considerably influenced by how much flights are delayed. But how should the delay be quantified with thousands of flights for each airport and airline? Here, we present a statistical analysis of arrival delays at several UK airports between 2018 and 2020. We establish a procedure to compare both mean delay and extreme events among airlines and airports, identifying a power-law decay of large delays. Furthermore, we note drastic changes in plane delay statistics during the COVID-19 pandemic. Finally, we find that delays are described by a superposition of simple distributions, leading to a superstatistics.