Ontology generation for flight safety messages in air traffic management

The analysis of various data is a challenging issue in the field of air traffic flow management (ATFM). Managing a large volume of data and their correct interpretation plays a key role in the prevention of air accidents, human errors, and flight interactions. In general, pilots and flight navigators deal with massive and variety information, including flight safety messages, meteorological messages, aviation notes for air man (NOTAMs) and etc. Therefore relationship between Heterogeneous big data is problem. Recently, the semantic web has been recognized as a novel foundation of knowledge management that relies on ontology (concept modeling language). The present study aimed to propose an efficient infrastructure for ATMs by defining the concepts, rules, and relationships between the concepts related to the scope of flight information. To this end, we designed and implemented the ontology of flight safety messages, which included an important part of flight operation information. In this research, the internet protocol suite (IPS)-based aviation networks and methontology methodology were exploited for the engineering of the ontology of flight messages. In addition, the implementation process was carried out using the Protégé software. The analysis of the system was performed using the actual data of Mashhad Airport, Iran. Finally, the implementation of the ontology of flight messages was demonstrated after a case study of several flight examples. The developed ontology could be incorporated into the foundation of an aeronautical telecommunication network (ATN) aviation network as an application.

Quantifying the Resilience of the U.S. Domestic Aviation Network During the COVID-19 Pandemic

This paper analyzes the impacts of COVID-19 pandemic on the United States air transportation network between March and August 2020. Despite dramatic reductions in flight and passenger volumes, the network remained robust and resilient against perturbation. Although 24% of airports closed, the reduction in network efficiency was only 5.1%, which means airlines continued to serve most destinations. A deeper analysis of airport closures reveals that 1) small peripheral airports were the most likely to be closed; 2) socio-economic and epidemiological factors characterizing the airport’s region such as income, income inequality, political leaning, and the number of observed COVID cases were not predictive of airport closure. Finally, we show that high network robustness has a downside: although emissions from United States air traffic in 2020 fell by 37.4% compared to 2019, mostly due to the drop in the number of flights, emissions per passenger doubled in the period April to August 2020 and increased eightfold in the week of April 5–11. This rise indicates inefficient use of resources by airlines.

The Adoption of Network-Centric Data Sharing in Air Traffic Management

Network-centric sharing of data between all Air Traffic Management (ATM) stakeholders can improve the aviation network substantially. The System Wide Information Management (SWIM) platform is a platform for the open sharing of all information between aircraft operators, airports, air navigation services providers (ANSPs), and meteorology services, but has struggled to find a following. This article aims to identify the potential reasons for the slow adoption of the SWIM platform, and to investigate how to better communicate its potential. To gain insight into the drivers for each of the stakeholders, a series of semi-structured interviews was conducted with airlines, airports and ANSPs. Moreover, an Airport Collaborative Decision Making (A-CDM) initiative at the airport in Dublin was included as a case study. Recommendations are provided on how to address the results from a governance point of view.