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Aviation safety reports that relate to loss of control in flight, problems that occur as a result of similar sounding alphanumerics, and pilot incapacitation are presented. Problems related to the go around maneuver in air carrier operations, and bulletins (and FAA responses to them) that pertain to air traffic control systems and procedures are included.
The Aviation Safety Reporting System (ASRS) in a partnership between the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration (FAA), participating carriers, and labor organizations. It is designed to improve the National Airspace System by collecting and studying reports detailing unsafe conditions and events in the aviation industry. Employees are able to report safety issues or concerns with confidentiality and without fear of discipline.
We present a study of weather-related incident reports submitted to NASA’s Aviation Safety Reporting System (ASRS) by air carrier pilots and air traffic controllers in the US. Using specific examples, we examine the weather-related risks reported and the relevant aspects of human performance and resilience management exhibited during these incidents. We describe our methods of lesson extraction to maximize learning, including the use of advance algorithms, and we conclude with a review of the weather-related risks encountered and the resilient practices employed to mitigate them.
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The aviation safety reporting system, an accident reporting system, is presented. The system identifies deficiencies and discrepancies and the data it provides are used for long term identification of problems. Data for planning and policy making are provided. The system offers training in safety education to pilots. Data and information are drawn from the available data bases.
Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants and other involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in a number of ways including entry into an online database, Safety Alert Bulletins and For Your Information Notices, and the CALLBACK newsletter. Key to these publications are the timely processing (de-identification, coding and summarization) of new reports, which is currently done by ASRS expert safety analysts. Thus, we believe topic modelling could decrease effort in ASRS, if topics are comprehensible. Aim: We propose a methodology to evaluate whether automated topic finding using topic modelling provides meaningful and useful topics. Method: We extend the total error survey methodology to evaluate user topic comprehension of machine learning outputs. To accomplish this we performed a literature review to identify existing methods and define a construct for topic comprehension, utilizing existing ASRS synopsis writing practices to more precisely define meaningfulness and usefulness. Results: A survey protocol was created that addresses the limitations of other survey protocols found in the literature review, which we found lacking in rationale and clear protocol definition. Conclusion: The surveying of user understanding in machine learning outputs presents challenges due to the explosion of parameters to control for and the lack of systematic approach presented in the literature. More reproducible work and survey protocols are needed in the literature and our work is one step towards that direction.
The NASA Aviation Safety Reporting System (ASRS) collects, analyzes, and distributes de-identified safety information provided through confidentially submitted reports from frontline aviation personnel. Since its inception in 1976, the ASRS has collected over 900,000 reports and has never breached the identity of the people sharing their information about events or safety issues. From this volume of data, the ASRS has released over 5,500 aviation safety alerts concerning potential hazards and safety concerns. The ASRS processes these reports, evaluates the information, and provides de-identified report information through the online ASRS Database at http://asrs.arc.nasa.gov. The NASA ASRS is also a founding member of the International Confidential Aviation Safety Systems (ICASS) group which is a collection of other national aviation reporting systems throughout the world. The ASRS model has also been replicated for application to improving safety in railroad, medical, fire fighting, and other domains. This presentation \vill discuss confidential, voluntary, and non-punitive reporting systems and their advantages in providing information for safety improvements.
The NASA Aviation Safety Reporting System (ASRS) collects, analyzes, and distributes de-identified safety information provided through confidentially submitted reports from frontline aviation personnel. Since its inception in 1976, the ASRS has collected over 1.4 million reports and has never breached the identity of the people sharing their information about events or safety issues. From this volume of data, the ASRS has released over 6,000 aviation safety alerts concerning potential hazards and safety concerns. The ASRS processes these reports, evaluates the information, and provides selected de-identified report information through the online ASRS Database at http:asrs.arc.nasa.gov. The NASA ASRS is also a founding member of the International Confidential Aviation Safety Systems (ICASS) group which is a collection of other national aviation reporting systems throughout the world. The ASRS model has also been replicated for application to improving safety in railroad, medical, fire fighting, and other domains. This presentation will discuss confidential, voluntary, and non-punitive reporting systems and their advantages in providing information for safety improvements.
The NASA Aviation Safety Reporting System (ASRS) collects, analyzes, and distributes de-identified safety information provided through confidentially submitted reports from frontline aviation personnel. Since its inception in 1976, the ASRS has collected over 1.4 M reports and has never breached the identity of the people sharing their information about events or safety issues. From this volume of data, the ASRS has released over 6,000 aviation safety alerts concerning potential hazards and safety concerns. The ASRS processes these reports, evaluates the information, and provides selected de-identified report information through the online ASRS Database at http:asrs.arc.nasa.gov. The NASA ASRS is also a founding member of the International Confidential Aviation Safety Systems (ICASS) group which is a collection of other national aviation reporting systems throughout the world. The ASRS model has also been replicated for application to improving safety in railroad, medical, fire fighting, and other domains. This presentation will discuss confidential, voluntary, and non-punitive reporting systems and their advantages in providing information for safety improvements.
The objectives of this evaluation were to: Identify the types of safety-related incidents reported to the Aviation Safety Reporting System (ASRS) in Emergency Medical Service (EMS) helicopter operations; Describe the operational conditions surrounding these incidents, such as weather, airspace, flight phase, time of day; and Assess the contribution to these incidents of selected human factors considerations, such as communication, distraction, time pressure, workload, and flight/duty impact.
A model has been developed which represents prominent reporter concerns expressed in the narratives of 300 mode-related incident reports from NASA's Aviation Safety Reporting System (ASRS). The model objectively quantifies the structure of concerns which persist across situations and reporters. These concerns are described and illustrated using verbatim sentences from the original narratives. Report accession numbers are included with each sentence so that concerns can be traced back to the original reports. The results also include an inventory of mode names mentioned in the narratives, and a comparison of individual and joint concerns. The method is based on a proximity-weighted co-occurrence metric and object-oriented complexity reduction.
The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.
The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.
Data obtained from the NASA Aviation Safety Reporting System (ASRS) data base were used to determine problems in general aviation single pilot IFR operations. The data examined consisted of incident reports involving flight safety in the National Aviation System. Only those incidents involving general aviation fixed wing aircraft flying under IFR in instrument meteorological conditions were analyzed. The data were cataloged into one of five major problem areas: (1) controller judgement and response problems; (2) pilot judgement and response problems; (3) air traffic control intrafacility and interfacility conflicts; (4) ATC and pilot communications problems; and (5) IFR-VFR conflicts. The significance of the related problems, and the various underlying elements associated with each are discussed. Previous ASRS reports covering several areas of analysis are reviewed.
While many existing taxonomies and frameworks provide a common vocabulary for describing how human operators fail in the context of sociotechnical systems, at present, there is no common vocabulary to describe how humans succeed. Such a framework would facilitate systematically collecting and analyzing data on how human performance can produce safety, not just how it can reduce safety. One potentially rich source of currently available information for exploring desired performance is the reports submitted to NASA’s Aviation Safety Reporting System (ASRS). These de-identified, confidential, and voluntary narrative reports are submitted by pilots, controllers, ground operators, and others within aviation operations. While these reports are primarily submitted to describe safety risks, incidents, and problems, they also often describe how those risks were mitigated, and provide a window into aspects of everyday work in aviation. These reports can be searched in a variety of ways. This paper describes methods for systematically examining ASRS narratives to understand how operators talk about their own resilient behaviors during adverse safety conditions and events. Guided by Erik Hollnagel’s Resilience Assessment Grid framework (i.e., anticipate, monitor, respond, learn), various approaches and tools for such inquiries are described. The approach, process, challenges, and suggestions to building an operator-based description of resilient performance are discussed, and tools to facilitate data analysis to maximize learning from these reports are described.
While many existing taxonomies and frameworks provide a common vocabulary for describing how human operators fail in the context of sociotechnical systems, at present, there is no common vocabulary to describe how humans succeed. Such a framework would facilitate systematically collecting and analyzing data on how human performance can produce safety, not just how it can reduce safety. One potentially rich source of currently available information for exploring desired performance is the reports submitted to NASA’s Aviation Safety Reporting System (ASRS). These de-identified, confidential, and voluntary narrative reports are submitted by pilots, controllers, ground operators, and others within aviation operations. While these reports are primarily submitted to describe safety risks, incidents, and problems, they also often describe how those risks were mitigated, and provide a window into aspects of everyday work in aviation. This paper describes an analysis of ASRS narratives to understand how operators talk about their own resilient behaviors during adverse safety conditions and events. Guided by Erik Hollnagel’s Resilience Assessment Grid framework (i.e., anticipate, monitor, respond, learn), we illustrate our approach and methodology with examples from reports. We also highlight some of the challenges and how further research is needed in developing a taxonomy of operators’ descriptions of resilient performance.
Increasing numbers of aircraft are operating on the surface of airports throughout the world. Airport operations are forecast to grow by more that 50%, by the year 2005. Airport surface movement traffic would therefore be expected to become increasingly congested. Safety of these surface operations will become a focus as airport capacity planning efforts proceed toward the future. Several past events highlight the prevailing risks experienced while moving aircraft during ground operations on runways, taxiways, and other areas at terminal, gates, and ramps. The 1994 St. Louis accident between a taxiing Cessna crossing an active runway and colliding with a landing MD-80 emphasizes the importance of a fail-safe system for airport operations. The following study explores reports of incidents occurring on an airport surface that did not escalate to an accident event. The Aviation Safety Reporting System has collected data on surface movement incidents since 1976. This study sampled the reporting data from June, 1993 through June, 1994. The coding of the data was accomplished in several categories. The categories include location of airport, phase of ground operation, weather /lighting conditions, ground conflicts, flight crew characteristics, human factor considerations, and airport environment. These comparisons and distributions of variables contributing to surface movement incidents can be invaluable to future airport planning, accident prevention efforts, and system-wide improvements.
The Aviation Safety Reporting System (ASRS) was established in 1976 under an agreement between the Federal Aviation Administration (FAA) and the National Aeronautics and Space Administration (NASA). This cooperative safety program invites pilots, air traffic controllers, flight attendants, maintenance personnel, and others to voluntarily report to NASA any aviation incident or safety hazard. The FAA provides most of the program funding. NASA administers the program, sets its policies in consultation with the FAA and aviation community, and receives the reports submitted to the program. The FAA offers those who use the ASRS program two important reporting guarantees: confidentiality and limited immunity. Reports sent to ASRS are held in strict confidence. More than 350,000 reports have been submitted since the program's beginning without a single reporter's identity being revealed. ASRS removes all personal names and other potentially identifying information before entering reports into its database. This system is a very successful, proof-of-concept for gathering safety data in order to provide timely information about safety issues. The ASRS information is crucial to aviation safety efforts both nationally and internationally. It can be utilized as the first step in safety by providing the direction and content to informed policies, procedures, and research, especially human factors. The ASRS process and procedures will be presented as one model of safety reporting feedback systems.