Aligning Aviation Safety Reporting System Anomaly Codes for Crew Communication and Coordination with Human Factors Taxonomies

<p>This study addresses the lack of theoretical grounding and definitions for the Aviation Safety Reporting System (ASRS) anomaly codes, particularly those related to pilot communication and coordination errors. To improve the conceptual consistency and analytical usability of ASRS data, the research developed a framework that maps ASRS codes to three established theory-based human factors taxonomies: the Human Factors Analysis and Classification System (HFACS), Threat and Error Management (TEM), and Aviation Causal Contributors for Error Reporting Systems (ACCERS). The study used qualitative text mining to systematically code communication and coordination errors submitted by Part 121 pilots for a stratified sample of 365 ASRS reports from 2007 to 2022. Additional analysis examined trends related to changes in pilot training and safety programs, while demographic data ensured sample representativeness.</p> <p>The resulting taxonomy framework revealed both overlapping and distinct emphases in how the taxonomies classify events, with each taxonomy offering unique strengths, TEM emphasizing error detail, ACCERS capturing influencing factors, HFACS focusing on human error, and ASRS documenting event types. Despite these differences, substantial alignment was found, with approximately 85% of interactively coded taxonomy results matching those derived through the framework, supporting its validity for cross-database analysis. Trends also showed increases in taxonomy coding rates and communication-related codes following the introduction of key safety initiatives like AQP and SMS, suggesting some influence on reporting practices.</p> <p>This framework contributes a structured, theory-informed method for aligning safety data across disparate taxonomies, enhancing the accuracy, efficiency, and depth of aviation safety analysis. It provides practical tools for analysts conducting cross-database searches and offers a foundation for developing a standardized industry taxonomy. The study recommends further expansion to include more error types and taxonomies, ultimately supporting future automation and machine learning applications for predictive safety analysis.</p>

Name thesis:degree_name

Doctor of Philosophy in Aviation

Level thesis:degree_level

Dissertation - Open Access

Discipline thesis:degree_discipline

College of Aviation

Year

2025

Author dc:creator

• Herr, Jennifer Rose

• communication and coordination
• taxonomy framework
• qualitative research
• error classification
• aviation safety
• pilot reporting
• cross-database analysis
• ASRS
• Aviation Safety and Security

Repository record dc:identifier

https://commons.erau.edu/edt/929

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1968