RUNWAY INCURSIONS IN NORWAY 2009-2019: A CASE STUDY OF EXTENDED INVESTIGATION REPORTS

The aim of the present study is to examine case studies from extended investigations of runway incursions in Norway from 2009-2019. Runway incursions involves an incorrect presence of an aircraft, vehicle or person on the runway and represents a relatively frequent threat to safety in the aviation industry. A content analysis and classification of the extended investigation reports (N=7) revealed 42 explanatory factors that could be condensed into four categories related to perception, procedural errors, memory and decision-making. When mapped onto the theoretical framework of situational awareness about 70% of the explanatory factors were related to misperception of information, improper comprehension of information or incorrect projection of future actions, respectively. The present study suggests that situational awareness can serve as a useful theoretical framework to identify deficits in human factors associated with runway incursion incidents. The results from this study contributes to inform investigation into aviation hazards and training of air traffic controllers.

Narrative Analysis of Runway Incursion Reports in the National Transportation Safety Board Database To Identify Contributing

A runway incursion occurs when an aircraft, ground vehicle, or a pedestrian is incorrectly present on the runway. This incorrect presence can lead to a collision resulting in fatal injuries and aircraft damage. Despite the aviation community’s measures to reduce incursions, they continue increasing. Most runway incursions are a result of human error. Our limited knowledge of these human errors and their causes is hindering our ability to reduce runway incursions. While previous researchers have analyzed past runway incursions to identify types of human error, we still know little about the causes of these errors. The narratives in the NTSB database often provide detailed information to identify human errors and their causes. In this paper, we analyze the narratives of runway incursion reports in the NTSB database. We use task analysis to map human error to tasks they perform, and map the causes of these errors to the error itself.

Using the Functional Resonance Analysis Method (FRAM) in Aviation Safety: A Systematic Review

The functional resonance analysis method (FRAM) is a system-based method to understand highly complex sociotechnical systems. Besides learning from safety occurrences or undesirable states, FRAM can be used to understand how things go well in a system, by identifying gaps between “work as imagined” (WAI) and “work as done” (WAD). FRAM is increasingly used in many domains and can enhance our understanding of a complex system and proposes strategies to refine the work design. This systematic review identified 108 FRAM research papers from 2006–2019. Most of these papers were conducted by European researchers and employed qualitative methods such as document analysis, interviews, and focus groups with subject matter experts (SMEs) and observations to develop WAI and WAD. Despite being used in healthcare, construction, and maritime sectors among others, aviation was the most commonly explored domain in FRAM studies. The 26 FRAM studies in aviation explored many aspects of the aviation industry, including Air Traffic Control (ATC) systems, cockpit operation, ground handling, maintenance, and a range of past safety incidents, like runway incursions. This paper also characterises the FRAM studies focused on aviation in terms of the common methods and steps used to build FRAM and the available software tools to build FRAM nets. Current FRAM illustrates its advantages in capturing the dynamic and nonlinear nature of complex systems and facilitates our understanding and continual improvement of complex systems. However, there are some critical issues in FRAM use and interpretation, such as the consistency of methods and complexity and reliability of data collection methods, which should be considered by researchers and FRAM users in industry.

DRAGON-V: Detection and Recognition of Airplane Goals with Navigational Visualization

We introduce Detection and Recognition of Airplane GOals with Navigational Visualization (DRAGON-V), a visualization system that uses probabilistic goal recognition to infer and display the most probable airport runway that a pilot is approaching. DRAGON-V is especially useful in cases of miscommunication, low visibility, or lack of airport familiarity which may result in a pilot deviating from the assigned taxiing route. The visualization system conveys relevant information, and updates according to the airplane's current geolocation. DRAGON-V aims to assist air traffic controllers in reducing incidents of runway incursions at airports.