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At least 55 records · Page 3

On Time Performance Pressure

Within many operations, the pressures for on-time performance are high. Each month, on-time statistics are reported to the Department of Transportation and made public. There is a natural tendency for employees under pressure to do their best to meet these objectives. As a result, pressure to get the job done within the allotted time may cause personnel to deviate from procedures and policies. Additionally, inadequate or unavailable resources may drive employees to work around standard processes that are seen as barriers. However, bypassing practices to enable on-time performance may affect more than the statistics. ASRS reports often highlight on-time performance pressures which may result in impact across all workgroups in an attempt to achieve on-time performance. Reporters often provide in-depth insights into their experiences which can be used by industry to identify and focus on the implementation of systemic fixes.

aviation safety↗

A summary and integration of research concerning single pilot IFR operational problems

A review of seven research studies pertaining to Single Pilot IFR (SPIFR) operations was performed. Two studies were based on questionnaire surveys; two based on National Transportation Safety Board (NTSB) reports; two were based on Aviation Safety Reporting System (ASRS) incident reports, and one report used event analysis and statistics to forecast problems. The results obtained in each study were extracted and integrated. Results were synthesized and key issues pertaining to SPIFR operations problems were identified. The research that was recommended by the studies and that addressed the key issues is catalogued for each key issue.

Chapman, G. C.↗

A summary and integration of research concerning single pilot IFR operational problems

A review of seven research studies pertaining to Single Pilot IFR (SPIFR) operations was performed. Two studies were based on questionnaire surveys, two were based on National Transportation Safety Board (NTSB) reports, two were based on Aviation Safety Reporting System (ASRS) incident reports, and one report used event analysis and statistics to forecast problems. The results obtained in each study were extracted and integrated. Results were synthesized and key issues pertaining to SPIFR operations problems were identified. The research that was recommended by the studies and that addressed the key issues is cataloged for each key issue.

Chapman, G. C.↗

Cleared for the visual approach: Human factor problems in air carrier operations

The study described herein, a set of 353 ASRS reports of unique aviation occurrences significantly involving visual approaches was examined to identify hazards and pitfalls embedded in the visual approach procedure and to consider operational practices that might help avoid future mishaps. Analysis of the report set identified nine aspects of the visual approach procedure that appeared to be predisposing conditions for inducing or exacerbating the effects of operational errors by flight crew members or controllers. Predisposing conditions, errors, and operational consequences of the errors are discussed. In a summary, operational policies that might mitigate the problems are examined.

Monan, W. P.↗

Safety Analysis of FMS/CTAS Interactions During Aircraft Arrivals

This grant funded research on human-computer interaction design and analysis techniques, using future ATC environments as a testbed. The basic approach was to model the nominal behavior of both the automated and human procedures and then to apply safety analysis techniques to these models. Our previous modeling language, RSML, had been used to specify the system requirements for TCAS II for the FAA. Using the lessons learned from this experience, we designed a new modeling language that (among other things) incorporates features to assist in designing less error-prone human-computer interactions and interfaces and in detecting potential HCI problems, such as mode confusion. The new language, SpecTRM-RL, uses "intent" abstractions, based on Rasmussen's abstraction hierarchy, and includes both informal (English and graphical) specifications and formal, executable models for specifying various aspects of the system. One of the goals for our language was to highlight the system modes and mode changes to assist in identifying the potential for mode confusion. Three published papers resulted from this research. The first builds on the work of Degani on mode confusion to identify aspects of the system design that could lead to potential hazards. We defined and modeled modes differently than Degani and also defined design criteria for SpecTRM-RL models. Our design criteria include the Degani criteria but extend them to include more potential problems. In a second paper, Leveson and Palmer showed how the criteria for indirect mode transitions could be applied to a mode confusion problem found in several ASRS reports for the MD-88. In addition, we defined a visual task modeling language that can be used by system designers to model human-computer interaction. The visual models can be translated into SpecTRM-RL models, and then the SpecTRM-RL suite of analysis tools can be used to perform formal and informal safety analyses on the task model in isolation or integrated with the rest of the modeled system. We had hoped to be able to apply these modeling languages and analysis tools to a TAP air/ground trajectory negotiation scenario, but the development of the tools took more time than we anticipated.

Nancy G. Leveson↗

Alternative Perspectives on Risk: Individual Differences in Problem Structuring

Team decision making involves contributions of multiple players toward a common goal. While much has been written about the importance of developing shared mental models in order for teams to work together effectively, little has been done to determine the value of alternative perspectives on problem solving and decision making. Early studies of expertise contrasted experts with novices and noted that the two groups differ in the way they structure problems and in their selection of information as salient. Little attention has been given to differences among experts who differ in their specializations. A series of experiments was conducted to determine: (1) what dimensions of flight-related problem situations pilots judge to be most important when making flight-relevant decisions; and (2) whether pilots in different crew positions differ in the way they interpret problems relating to flight decisions. A sorting task was used to identify underlying dimensions judged as salient to individual pilots. Captains, first officers, and flight engineers from two major carriers participated in the study. Twenty-two flight scenarios were developed based on ASRS reports. Pilots were required to make judgments about how they would respond in each case and to sort the scenarios on the basis of similarity of decision factors. They were also asked to provide a verbal label that described each of their sorted categories. A second study required a different group of pilots (also captains, first officers and flight engineers) to sort on predetermined bases.

Orasanu, Judith↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA aviation safety reporting system

The origins and development of the NASA Aviation Safety Reporting System (ASRS) are briefly reviewed. The results of the first quarter's activity are summarized and discussed. Examples are given of bulletins describing potential air safety hazards, and the disposition of these bulletins. During the first quarter of operation, the ASRS received 1464 reports; 1407 provided data relevant to air safety. All reports are being processed for entry into the ASRS data base. During the reporting period, 130 alert bulletins describing possible problems in the aviation system were generated and disseminated. Responses were received from FAA and others regarding 108 of the alert bulletins. Action was being taken with respect to 70 of the 108 responses received. Further studies are planned of a number of areas, including human factors problems related to automation of the ground and airborne portions of the national aviation system.

Billings, C. E.↗

Crew Factors in Flight Operations XIV: Alertness Management in Regional Flight Operations Education Module

Regional operations encompass a broad range of pilots and equipment. This module is intended to help all those involved in regional aviation, including pilots, schedulers, dispatchers, maintenance technicians, policy makers, and others, to understand the physiological factors underlying fatigue, how flight operations affect fatigue, and what can be done to counteract fatigue and maximize alertness and performance in their operations. The overall purpose of this module is to promote aviation safety, performance, and productivity. It is intended to meet three specific objectives: (1) to explain the current state of knowledge about the physiological mechanisms underlying fatigue; (2) to demonstrate how this knowledge can be applied to improving flight crew sleep, performance, and alertness; and (3) to offer strategies for alertness management. Aviation Safety Reporting System (ASRS) and National Transportation Safety Board (NISH) reports are used throughout this module to demonstrate that fatigue is a safety issue in the regional operations community. The appendices at the end of this module include the ASRS reports used for the examples contained in this publication, brief introductions to sleep disorders and relaxation techniques, summaries of relevant NASA publications, and a list of general readings on sleep, sleep disorders, and circadian rhythms.

Rosekind, Mark R.↗

Crew Factors in Flight Operations XV: Alertness Management in General Aviation Education Module

Regional operations encompass a broad range of pilots and equipment. This module is intended to help all those involved in regional aviation, including pilots, schedulers, dispatchers, maintenance technicians, policy makers, and others, to understand the physiological factors underlying fatigue, how flight operations affect fatigue, and what can be done to counteract fatigue and maximize alertness and performance in their operations. The overall purpose of this module is to promote aviation safety, performance, and productivity. It is intended to meet three specific objectives: (1) to explain the current state of knowledge about the physiological mechanisms underlying fatigue; (2) to demonstrate how this knowledge can be applied to improving flight crew sleep, performance, and alertness; and (3) to offer strategies for alertness management. Aviation Safety Reporting System (ASRS) and National Transportation Safety Board (NISH) reports are used throughout this module to demonstrate that fatigue is a safety issue in the regional operations community. The appendices at the end of this module include the ASRS reports used for the examples contained in this publication, brief introductions to sleep disorders and relaxation techniques, summaries of relevant NASA publications, and a list of general readings on sleep, sleep disorders, and circadian rhythms.

Rosekind, Mark R.↗

Extracting Lessons of Resilience Using Machine Mining of the ASRS Database

NASA’s Aviation Safety Reporting System (ASRS) database is the world's largest repository of voluntary, confidential safety information provided by aviation's frontline personnel, including pilots, air traffic controllers, mechanics, flight attendants, dispatchers, and other members of the aviation community and the public. The database contains close to 2 million narratives, many of which describe everyday situations in which people saved the day. In these situations, people’s resilient behavior solved a problem, dealt with a malfunction, and maintained a safe operation despite a serious perturbation. To be able to extract lessons of such resilience from this large database, the use of machine learning algorithms is being explored. In this report, we describe a comparison between two such algorithms: Perilog and Word2Vec. An identical search using both programs was done on a database containing approximately 470,000 ASRS reports submitted between 1988 and 2022. The comparison reveals some of the strength and weaknesses of each algorithm as well as the challenges inherent in using such algorithms to extract lessons of resilience from the ASRS database.

resilience↗

Assessing the use of UAS-related terms in ASRS using Seed Topic Modeling

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. The de-identified reports are then disseminated to the aviation community in a number of ways including entry into an online database. Augmenting the discovery of topics of user interest in this online database would therefore be beneficial to the community it serves. Aim: We propose and execute an experiment to assess the use of seed term topic modeling using the database narratives to identify UAS reports. The use of seed term topic modeling would enable users to identify groups of related narratives associated to a topic of their interest. Method: We use a newly curated field in ASRS reports which identify UAS from non-UAS reports in combination of different set of UAS related terms to assess if seed topic modeling can be used in ASRS.

LDA↗

A Survey Protocol to Assess Meaningfulness and Usefulness of Automated Topic Finding in the NASA Aviation Safety Reporting System

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.

topic finding↗

Ground Operations and ASRS

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

human factors↗

NASA aviation safety reporting system

During the second quarter of the Aviation Safety Reporting System (ASRS) operation, 1,497 reports were received from pilots, controllers, and others in the national aviation system. Details of the administration and results of the program to date are presented. Examples of alert bulletins disseminated to the aviation community are presented together with responses to those bulletins. Several reports received by ASRS are also presented to illustrate the diversity of topics covered by reports to the system.

Source record↗

Coordination Between Sectors in Shared Airspace Operations

Recent studies have shown that a more efficient use of airspace may involve shared airspace operations, i.e., temporal as well as spatial separation of arrival and departure flows [1][2]. Temporal separation would permit a departure aircraft to fly through an arrival flow, depending on an available gap. This would necessitate careful and precise coordination between controllers in different sectors. Three methods of coordination which permit the penetration of a controller's airspace by another controller's aircraft are described: point out, look-and-go, and prearranged coordination procedure. Requirements of each method are given, along with associated problems that have surfaced in the field as described by Aviation Safety and Reporting System (ASRS) and other reports. A Human-in-the-Loop simulation was designed to compare two of the methods: point out and prearranged coordination procedures. In prearranged coordination procedures (P-ACP), the controllers control an aircraft in another controller's airspace according to specified prearranged procedures, without coordinating each individual aircraft with another controller, as is done with point outs. In the simulation, three experienced controllers rotated through two arrival sectors and a non-involved arrival sector of a Terminal Radar Approach Control (TRACON) airspace. Results of eighteen one-hour simulation runs (nine in each of the two conditions) showed no impact of the coordination method on separation violations nor on arrival times for 208 departing aircraft crossing an arrival stream. Participant assessment indicated that although both coordination conditions were acceptable, the prearranged coordination procedure condition was slightly safer, more efficient, timely, and overall, worked better operationally. Problems arose in the point out condition regarding controllers noticing acceptance of point outs. Also, in about half of the point-out runs, time pressure was felt to have had an impact on when and if the departures could cross an arrival stream. An additional problem with point outs may be confusion in the field about which controller has responsibility for separating point-out aircraft from other aircraft.

Parke, Bonny↗