According to the International Civil Aviation Organization, the world aviation air traffic has grown by an average yearly rate of 5% over the last thirty years, until the devastating downturn brought on by the COVID crisis of 2020. Regardless of the current situation, there are still a number of issues and challenges that the industry is confronted with, not the least of which are related to sustainability, the conversion to electrical usage, the challenge of increasing propulsion efficiency in conventional propulsion, the digital transformation of the entire ecosystem, etc. In response, system developers and researchers in the field are working on a number of key technologies and methodologies to solve some of these issues. The Sustainable Aviation Research Society (SARES), a global organization that seeks to encourage research in this area and helps disseminate knowledge via conferences and symposia, has been organizing meetings to promote sustainable aviation over the five years. Three of these are the International Symposium on Sustainable Aviation (ISSA), International Symposium on Electric Aviation and Autonomous Systems (ISEAS), and the International Symposium on Aircraft Technology, MRO, and Operations (ISATECH).
Communication problems are acknowledged as hazardous eventualities affecting operations negatively. However, a few systematic attempts have been made to understand the pattern of communication issues and their contribution to safety events. In this paper, we present the AVAC-COM communication model and taxonomy based on the cybernetics approach and a literature review. The model elements and taxonomy variables regard the actors, signals, coders, interference, direction and timing, predictability, decoders, and channels. To test the applicability and potential value of the AVAC-COM framework, we analysed 103 safety investigation reports from aviation published between 1997 and 2016 by the respective authorities of Canada, the United States, Australia, the United Kingdom and the Netherlands. The overall results of the 256 cases of communication flaws detected in the reports suggested that these regarded more frequently Human-Media and Human-Human interactions, verbal and local communications as well as unfamiliarity of the receivers with the messages transmitted. Further statistical tests revealed associations of the region, time period, event severity and operations type with various variables of the AVAC-COM taxonomy. Although the findings are only indicative, they showed the potential of the AVAC-COM model and taxonomy to be used to identify strong and weak communication elements and relationships in documented data such as investigation and hazard reports.
The objective of the study described in this paper is to define safety metrics that are based on the effectiveness of risk controls. Service providers define and implement such risk controls in order to prevent hazards developing into an accident. The background of this research is a specific need of the aviation industry where small and medium-sized enterprises lack large amounts of safety-related data to measure and demonstrate their safety performance proactively. The research department of the Aviation Academy has initiated a 4-year study, which will test the possibility to develop new safety indicators that will be able to represent safety levels proactively without the benefit of large data sets. As part of the development of alternative safety metrics, safety performance indicators were defined that are based on the effectiveness of risk controls. ICAO (2013) defines a risk control as “a defence with specific mitigation actions, preventive controls or recovery measures put in place to prevent the realization of a hazard or its escalation into an undesirable consequence”. Examples of risk controls are procedures, education and training, a piece of equipment etc. It is crucial for service providers to determine whether the introduced risk controls are indeed effective in reducing the targeted risk. ICAO (2013) describes the effectiveness of risk control as "the extent to which the risk control reduces or eliminates the safety risks”, but does not provide guidance on how to measure the effectiveness of risk control. In this study, a generic metrics for the effectiveness of risk controls based on their effectiveness was developed. The definition of the indicators allows, for each risk control, derivation of specific indicators based on the generic metrics. The suitability of the metrics will subsequently be tested in pilot studies within the aviation industry.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
