Improving the level of psychological preparation of potential drivers
DOI: 10.1051/matecconf/202134100019
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the critical issue of road traffic safety, specifically focusing on the psychological preparation of drivers to reduce accident rates. The authors argue that over 75% of road accidents in Russia are caused by driver violations of traffic regulations, often stemming from low transport culture and insufficient psychological reliability. With approximately 17,000 deaths recorded in 2019 and significant long-term disability rates, the study highlights the urgent need for an integrated approach to driver safety. The research is motivated by the observation that while professional drivers undergo regular advanced training, amateur drivers receive no further psychological support after obtaining their licenses, relying solely on punitive measures like fines. The authors posit that analyzing individual psychological characteristics and predispositions to emergency situations is a vital reserve for improving preventive activities in road safety management. To address this problem, the authors propose a comprehensive methodology for assessing and improving the psychophysiological suitability of drivers across four distinct stages: admission to driving school, the training period, the beginning of professional activity, and ongoing professional activity. The core of the proposed solution involves a mechanism for evaluating professional driver suitability using specific psychophysiological indicators. This includes the creation of a databank to record professionally important qualities (PIQ) and the use of digital and information technologies to monitor drivers’ states. The study outlines algorithms for selecting candidates based on their psychophysiological signs of suitability, conditional suitability, or unsuitability. For existing personnel, the methodology suggests rational reshuffling, such as transferring drivers with lower psychophysiological indicators to more monotonous routes, contingent on health status and consent. The assessment methodology employs a specific set of research tools to evaluate 18 psychological and psychophysiological functions, including intellectual development, emotional stability, anxiety, visual-motor response, and attention stability. These are measured using standardized tests such as Raven’s Matrices, the 16PF personality questionnaire, the Taylor Manifest Anxiety Scale, and various instrumental tests like reflexometry and correction tasks. Additionally, the paper identifies specific technical means for enhancing psychological reliability, including the UPFT-1/30, PAKPF-02, and ROFES complexes for recording functional and emotional states, as well as the TA-2 psychophysiological trainer. These tools allow psychologists and instructors to objectively assess errors during practical exams and provide individualized training to novice drivers. The significance of this work lies in its proposal for a systematic, technology-driven approach to driver selection and training that prioritizes human safety and social stability. By integrating psychophysiological monitoring into the driver lifecycle, the authors argue that it is possible to eliminate causes of violations and increase the efficiency of labor resources. The proposed system aims to improve the quality of transport management and reduce emergency situations by fostering an evolutionary approach to personality development in professional contexts. Ultimately, the study suggests that leveraging digital technologies for psychological assessment can provide high-level transport support for the national economy while significantly enhancing road safety.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-18 |
| archive | success | canonical_url | — | — | 1 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-18 |
| chunk | success | chunk | — | — | 1 | 2026-06-18 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-18 |
| promote | success | — | — | — | 1 | 2026-06-18 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-18 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Methodological Resource: validation psychometrics
- Theoretical Contribution: computational model