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.
This article describes the relation between mental health and academic performance during the start of college and how AI-enhanced chatbot interventions could prevent both study problems and mental health problems.
Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam University of Applied Sciences, in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht, and the Metropolitan Region of Amsterdam Electric, developed both the back- and front-end of a charging infrastructure assessment platform that processes and represents real-life charging data. Charging infrastructure planning and design methods described in the literature use geographic information system data, traffic flow data of non-EV vehicles, or geographical distributions of, for example, refueling stations for combustion engine vehicles. Only limited methods apply real-life charging data. Rolling out public charging infrastructure is a balancing act between stimulating the transition to zero-emission transport by enabling (candidate) EV drivers to charge, and limiting costly investments in public charging infrastructure. Five key performance indicators for charging infrastructure utilization are derived from literature, workshops, and discussions with practitioners. The paper describes the Data Warehouse architecture designed for processing large amounts of charging data, and the web-based assessment platform by which practitioners get access to relevant knowledge and information about the current performance of existing charging infrastructure represented by the key performance indicators developed. The platform allows stakeholders in the decision-making process of charging point installation to make informed decisions on where and how to expand the already existing charging infrastructure. The results are generalizable beyond the case study regions in the Netherlands and can serve the roll-out of charging infrastructure, both public and semi-public, all over the world.
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.
