Psycholinguistics in Meeting the Challenges of Advanced Aeromobility and Organizing Safe Air Traffic in Low-Altitude Aviation

Anayatova, Raziyam; Ryabchenko, Inna; Tulekova, Gulnaz; Левченко, Наталія Михайлівна; Koshekov, Abay · 2025 · OpenAlex-citations

DOI: 10.31470/2309-1797-2025-37-2-6-32

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Summary

This study addresses the critical challenge of ensuring flight safety in low-altitude aviation within the Republic of Kazakhstan, particularly in the context of emerging advanced aeromobility technologies like eVTOLs and air taxis. The research is motivated by the high incidence of aviation accidents linked to pilot-air traffic control (ARC) communication errors and the specific linguistic complexities of Kazakhstan’s multilingual environment. As Kazakhstan transitions its civil aviation operations to the state language (Kazakh), significant dialectal variations across regions pose a risk of misunderstanding during radio communications. The authors aim to assess how linguistic factors, alongside traditional variables, impact flight safety and to develop a predictive model for identifying safety threats. The researchers employed a triangulation approach combining qualitative content analysis of scientific literature with empirical methods, including a natural psycholinguistic experiment and mathematical modeling. The empirical study involved 315 participants from various aviation entities in Kazakhstan, including pilots, air traffic controllers, and airport staff, conducted between 2021 and 2024. Data collection included observing professional communication discourse and analyzing phonetic features of key radio turns. To quantify safety risks, the authors constructed a logistic regression model using the maximum likelihood method. This model estimated the probability of air accidents based on seven variables: language dialect, crew experience, weather conditions, cloud cover, wind speed, precipitation intensity, and communication quality. The results identified specific linguistic discrepancies in Kazakh radio phraseology that could lead to misinterpretation, such as errors in pronunciation or sentence structure. The logistic regression analysis revealed that dialect differences (coefficient 0.979) and poor communication quality (coefficient -1.969, indicating risk reduction with better quality) are significant predictors of accident probability. Other factors, including difficult weather, heavy precipitation, and high wind speeds, also positively correlated with increased risk. The study established a threat gradation system ranging from a "Green Zone" (low probability, 0–0.2) to a "Red Zone" (critical probability, 0.9–1.0). Predictive experiments demonstrated that scenarios involving dialect barriers, adverse weather, and poor communication could push accident probabilities into the "Orange" or "Red" zones, necessitating immediate safety interventions. The significance of this research lies in the development of an integrated model for predicting air traffic safety threats that incorporates the innovative "dialect factor" alongside traditional operational variables. The findings suggest that ignoring linguistic nuances, particularly in multinational crews or regions with significant dialect differences, poses a substantial safety risk. The authors conclude that Latinizing aviation terminology and strictly adhering to standardized phraseology are essential for mitigating these risks. This model provides a practical tool for establishing flight safety threat levels and preventing incidents in low-altitude aviation, supporting Kazakhstan’s broader goals for safe aeromobility and compliance with international aviation security standards.

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• situation awareness theory