Aviation Safety Reporting System Database Research Articles

Natural Language Processing of Aviation Safety Reports to Identify Inefficient Operational Patterns

With the growth in commercial aviation traffic and the need for improved environmental performance, strategies to lower emissions that can be implemented in the near term are necessary. Since novel technology takes time to enter the market, operational improvements that employ existing aircraft and require no new infrastructure are fit for this goal. While quantified data collected throughout aviation, such as arrival/departure statistics and flight data, have been well-utilized, text data collected through safety reports have not been leveraged to their full extent. In this paper, a methodology is presented that can use aviation text data to identify high-level causes of flight delays and cancellations, using delays as a metric of operational inefficiency. The dataset is extracted from the Aviation Safety Reporting System (ASRS), which includes voluntary safety incident reports in text narrative and metadata formats. The methodology uses natural language processing tools, K Means clustering, and dimensionality reduction by t-Distributed Stochastic Neighbor Embedding (t-SNE) to categorize and visualize narratives. The method identified 7 major clusters and a total of 23 sub-clusters. A comparison between the subclusters’ topics and the causes of flight delays revealed by the quantified data shows that the ASRS database provides a unique safety perspective to delay cause identification, as illustrated by the method’s identification of maintenance as the main cause of delays, rather than weather.

Setting up new standards in aviation industry with the help of artificial intelligent – machine learning application

In these times, dominated by innovation, technology is an important asset in optimizing maintenance, aircraft operations and even the investigation process of aviation accidents, with unprecedented results so far. All the information related to an aircraft, generated over time, can be included in a database, in order to be analyzed for better technical insights. The same approach can be applied in analyzing evidence and reports generated after an aircraft accident occurred. Aircraft investigation involves tones of hard-working hours and evidence analysis. This research aims to identify a new tool to improve the investigation process of aircraft incidents caused by human factors errors. Using artificial intelligence to process the resulted evidence during the aircraft accidents investigation, will shed light on the root cause of the incident much faster. Accelerating an investigation means huge benefits for all parties involved. In this sense, a proof-of-concept software has been developed to emphasize the power of artificial intelligent in the aircraft accidents investigation. Using machine learning algorithms, a program was trained with data, to develop an application capable of automatically determining the root cause of an accident based on witness statements. All data was derived from the Aviation Safety Reporting System database from the U.S, which was queried for incidents, occurred between 2000 and 2020. This application is not only about automating a stage in an aircraft accident investigation, is rather about setting up new standards in aviation industry with the help of artificial intelligence.

Temporal topic modeling applied to aviation safety reports: A subject matter expert review

The aviation safety reporting system database has seen many applications of topic modeling and natural language processing. Its size, metadata, and narratives have made it ideal for demonstrating new models and approaches. However, less often seen is how these methods are applied by subject matter experts and subsequently interpreted. Within this article, latent Dirichlet allocation is applied to a fourteen year sample of the aviation safety reporting system database, filtered for commercial airline operations. The topics terms generated were presented to three subject matter experts, along with weighted topic narratives, and mean usage of each topic by month. A high level of inter-rater agreement was demonstrated for the conceptual themes that emerged from the selected topics. The subject matter experts were able to identify the reporter’s qualification (air traffic controllers, etc.) as well as some of the environmental, regulatory, and industry factors consistent with observed temporal trends of topic usage. Through this examination, additional suggestions are made for the process of interpretation of topic modeled themes, expanding on the work of DiMaggio, Nag, and Blei. Post facto, the study identified the increase in reported safety concerns facing flight crews following the introduction of the runway awareness and advisory system into the cockpit in 2009. Additionally, the results precipitated a change to the way the subject matter experts interpreted the narratives and their implications to industry safety. This work provides a practical validation of the use of natural language processing in sensemaking and pinpointing trends necessary for prioritizing safety activities.

Visual representation of safety narratives

A computational method for the visualization of text-based safety narratives on a two dimensional plane is shown. This multi-step approach utilizes latent semantic analysis to first infer higher-order structures and then isometric mapping to reduce the projection to two dimensions. Metadata may then be overlaid on the projection. Demonstrated is the application of this process to the human coded primary-problems identified and the phase of flight for a sample of the Aviation Safety Reporting System database. It is evident that this approach provides additional insight for the analysis of large inter-related corpora commonly found in safety programs.

Pilot/ATC Communication Conflicts: Implications for NextGen

In the planned NextGen aviation operations, it will be critical to ensure shared situational understanding and cooperative problem solving between aircrews and ATC (Air Traffic Controllers). A first step in predicting how future changes will impact flight crews and ATC is to examine the present system and to pinpoint problematic areas that may be ameliorated or exacerbated by advanced automation and heavier traffic density. Method: We coded ASRS (Aviation Safety Reporting System) reports identified as having communication conflicts between pilots and ATC. Results: Results describe types of conflict, operational context, phase of flight, operator states, and situations conducive to communication conflicts, risk perception differences and inappropriate resolution strategies. Limitations - Characteristics of the ASRS database guarantee that it is a rich source of data, but also impose inherent limitations with respect to generalization. Implications: This research will enable us to better predict NextGen aircrew/ATC communication breakdowns and conflicts resulting from specific situations and/or operator states.

Investigation of Alarm-Related Accidents and Incidents in Aviation

With increases in technology, pilots have assumed the role of system monitors. As such, they have become dependent upon sensor-based alarm signals, some of which are unreliable. Researchers have shown that unreliable alarms foster mistrust and complacency by operators, and may degrade flight tasks and alarm reactions. To supplement available laboratory investigations, we tabulated the number of events involving true, false, and missed alarms within the Aviation Safety Reporting System database, the National Transportation Safety Board database, and the U.S. Army's Aviation Safety database. We also separated the results by aircraft type, avionics, flight phase, and specific problem. Our investigation revealed that alarm-related problems occur frequently across flight environments, and that false alarms remain a significant concern in aviation. Given the pervasiveness of alarm-related problems, it is important that future researchers devote considerable effort to improve aviation alarm systems.

Alarm Related Incidents in Aviation: A Survey of the Aviation Safety Reporting System Database

Today's aircraft cockpits are complex environments requiring pilots to monitor a large number of automated displays and alarm systems. A recent investigation of alarm-related incidents in Naval aircraft found high rates of alarm-related problems, particularly false and missed alarms. For the current research, we analyzed a sample of narrative accounts taken from the National Aeronautics and Space Administration's Aviation Safety Reporting System (ASRS). We categorized and tabulated the data according to alarm problem (false, missed, or other), aircraft type, avionics (alarm system), and flight phase. Our findings indicated that alarm-related incidents are still quite common in civil aircraft. Furthermore, most alarm-related incidents occur during the approach phase of a flight, and are likely to involve the Ground Proximity Warning System (GPWS) or the Traffic Collision Avoidance System (TCAS). We recommend further empirical research to understand alarm-related problems, particularly the cry-wolf effect.