A literature review conducted as part of a research project named “Measuring Safety in Aviation – Developing Metrics for Safety Management Systems” revealed several challenges regarding the safety metrics used in aviation. One of the conclusions was that there is limited empirical evidence about the relationship between Safety Management System (SMS) processes and safety outcomes. In order to explore such a relationship, respective data from 7 European airlines was analyzed to explore whether there is a monotonic relation between safety outcome metrics and SMS processes, operational activity and demographic data widely used by the industry. Few, diverse, and occasionally contradictory associations were found, indicating that (1) there is a limited value of linear thinking followed by the industry, i.e., “the more you do with an SMS the higher the safety performance”, (2) the diversity in SMS implementation across companies renders the sole use of output metrics not sufficient for assessing the impact of SMS processes on safety levels, and (3) only flight hours seem as a valid denominator in safety performance indicators. At the next phase of the research project, we are going to explore what alternative metrics can reflect SMS/safety processes and safety performance in a more valid manner
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As part of their SMS, aviation service providers are required to develop and maintain the means to verify the safety performance of their organisation and to validate the effectiveness of safety risk controls. Furthermore, service providers must verify the safety performance of their organisation with reference to the safety performance indicators and safety performance targets of the SMS in support of their organisation’s safety objectives. However, SMEs lack sufficient data to set appropriate safety alerts and targets, or to monitor their performance, and no other objective criteria currently exist to measure the safety of their operations. The Aviation Academy of the Amsterdam University of Applied Sciences therefore took the initiative to develop alternative safety performance metrics. Based on a review of the scientific literature and a survey of existing safety metrics, we proposed several alternative safety metrics. After a review by industry and academia, we developed two alternative metrics into tools to help aviation organisations verify the safety performance of their organisations.The AVAV-SMS tool measures three areas within an organisation’s Safety Management System:• Institutionalisation (design and implementation along with time and internal/external process dependencies).• Capability (the extent to which managers have the capability to implement the SMS).• Effectiveness (the extent to which the SMS deliverables add value to the daily tasks of employees).The tool is scalable to the size and complexity of the organisation, which also makes it useful for small and medium-sized enterprises (SMEs). The AVAS-SCP tool also measures three areas in the organisation’s safety culture prerequisites to foster a positive safety culture:• Organisational plans (whether the company has designed/documented each of the safety cultureprerequisites).• Implementation (the extent to which the prerequisites are realised by the managers/supervisors acrossvarious organisational levels).• Perception (the degree to which frontline employees perceive the effects of managers’ actions relatedto safety culture).We field-tested these tools, demonstrating that they have adequate sensitivity to capture gaps between Work-as-Imagined (WaI) and Work-as-Done (WaD) across organisations. Both tools are therefore useful to organisations that want to self-assess their SMS and safety culture prerequisite levels and proceed to comparisons among various functions and levels and/or over time. Our field testing and observations during the turn-around processes of a regional airline confirm that significant differences exist between WaI and WaD. Although these differences may not automatically be detrimental to safety, gaining insight into them is clearly necessary to manage safety. We conceptually developed safety metrics based on the effectiveness of risk controls. However, these could not be fully field-tested within the scope of this research project. We recommend a continuation of research in this direction. We also explored safety metrics based on the scarcity of resources and system complexity. Again, more research is required here to determine whether these provide viable solutions.
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This paper presents an alternative way to use records from safety investigations as a means to support the evaluation of safety management (SM) aspects. Datasets from safety investigation reports and progress records of an aviation organization were analyzed with the scope of assessing safety management’s role, speed of safety communication, timeliness of safety investigation processes and realization of safety recommendations, and the extent of convergence among SM and investigation teams. The results suggested an interfering role of the safety department, severe delays in safety investigations, timely implementation of recommendations, quick dissemination of investigation reports to the end-users, and a low ratio of investigation team recommendations included in the final safety investigation reports. The results were attributed to non-scalable safety investigation procedures, ineffective resource management, lack of consistent bidirectional communication, lack of investigators’ awareness about the overall organizational context, and a weak commitment of other departments to the realization of safety recommendations. The set of metrics and the combination of quantitative and qualitative methods presented in this paper can support organizations to the transition towards a performance-based evaluation of safety management.
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In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
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.
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
