Analysis and Interview Survey to Detect Subjective Fatigue and Accident risk of Truck Drivers

Abstract

[Methods] In this study, interview survey and analysis were conducted with the aim of detecting accident risk from subjective health data of drivers. For interview survey, we observed time-series changes in VAS for 1956 individuals over a year and selected 11 drivers who showed a significant worsening of subjective health. The 11 drivers were asked two types of questions. The first was, "Are there any factors that you are aware of about the period of time during which you showed significant worsening?" The second was, "What factors do you think are present that could have a dangerous effect on your driving?" The Analysis phase of the study examined whether the use of subjective health information and additional information would be useful in detecting accident risk. From the first question, we defined four patterns of VAS worsening trends and analyzed the relationships between these patterns and accident risk. From the second question, the index "change in the time of work start" was derived as a factor that many drivers consider dangerous. We then analyzed the correlation between this new index and the near-miss rate. [Results] for the relationship between VAS and accident risk, it was found that two of the four VAS risk patterns had a significant negative correlation with the rate of near-misses. Furthermore, analysis of the relationship between “change in the time of work start” and the accident risk revealed a significant negative correlation when the absolute value of “change in the time of work start” was within ±6 hours. This means that the rate of near-misses increases when the workday starts earlier than the previous day. [Conclusion] To detect a hazard leading to a driving near-miss with the VAS data alone, the worsening would have to continue for long time over 4 or 8 weeks. However, the newly discovered convince indicator "change in the time of work start" is a feature with a short span, and its addition to the VAS and accident risk analysis may improve the accuracy of health risk detection.

Keywords: Fatigue, Stress, Risk Management, Visual Analogue Scale, Driving Accidents

DOI: 10.54941/ahfe1005465