Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction

Fan, Chaojie; Huang, Shufang; Lin, Shuxiang; Xu, Diya; Peng, Yong; Yi, Shengen

doi:10.1155/2022/8328077

Cited by 18 publications

(6 citation statements)

References 63 publications

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“…(ECG). These methods can describe the drivers' condition with high accuracy, but these methods require a tool that is attached to a certain body part, so it could interfere with the movement while working (Fan et al 2022).…”

Section: Resultsmentioning

confidence: 99%

Physical Workload and Work Fatigue among Tank Car Drivers at Surabaya Integrated Terminal

Milinia

Haq

Alia

2022

FMI

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Highlights: Physical workload had no correlation with work fatigue among tank car drivers. Companies and tank car drivers are advised to carry out work fatigue management by exercising lightly and fulfilling nutritional and water intake. Abstract : Every year, there are nearly a thousand times more of non-fatal accidents than fatal accidents. In Indonesia, Java Island has the highest number of traffic accidents. Work accidents have three main factors, i.e. worker factors, labor factors, and environmental factors. Fatigue is one of the conditions called silent killer. The aim of this research was to analyze the correlation of physical workload with work fatigue in tank car drivers at Surabaya Integrated Terminal. This research was an observational cross-sectional study. The sample were 205 tank car drivers fufilling the inclusion criteria as fuel tank car drivers and willing to participate in this research. The independent variable in the study was physical workload, while the dependent variable was subjective work fatigue. Data were collected using the IFRC questionnaire. The Spearman’s correlation test revealed the value of Sig. (2-tailed)=0.437, indicating no significant correlation between physical workload and subjective work fatigue. The results showed that the physical workload had no correlation with work fatigue. Fatigue found among the drivers are probably induced by other factors not analyzed in this study. Companies and tank car drivers are advised to carry out work fatigue management by, for example, drinking water regularly, carrying out light exercise, and fulfilling nutritional intake.

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Section: Resultsmentioning

confidence: 99%

Physical Workload and Work Fatigue among Tank Car Drivers at Surabaya Integrated Terminal

Milinia

Haq

Alia

2022

FMI

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“…Due to the high occurrence rate of rail accidents and the resulting socioeconomic consequences in today's world, it is necessary to pay more attention to possible causes or effective factors. Studies have shown that human factors are among the most important factors affecting these accidents (36,37). This issue becomes much more important considering the rights of each individual to have health and satisfaction in life.…”

Section: Discussionmentioning

confidence: 99%

Shift Work as a Risk Factor for Poor Sleep Quality and Daytime Sleepiness in Khorasan Railway Personnel in Iran

et al. 2022

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Background: Shift work disrupts the sleep rhythm and leads to daily sleepiness and physical and mental complications. Objectives: This study aimed to investigate the predictive role of shift work in poor sleep quality and daytime sleepiness in railway personnel. Methods: This descriptive-analytical and cross-sectional study was conducted on the employees of the Khorasan railway in Iran in 2021, 450 individuals of whom were selected as a sample using the census sampling method. The data collection method was through a checklist containing demographic information and related factors (e.g., employment history) and through a physician to evaluate underlying diseases, body mass index (BMI), smoking and use of substances (past medical history), and completion of standard questionnaires, including the Pittsburgh Sleep Quality Index (PSQI), Karolinska Sleepiness Scale (KSS), and STOP-BANG questionnaire. The data were analyzed using the t-test and simultaneous multivariate regression analysis by SPSS software (version 24). Results: Out of 450 employees, 144 (32%) and 306 (68%) subjects were day workers and shift workers, respectively. The average age of the participants was 40 ± 5.97 years. A significant difference was observed between the BMI with PSQI, KSS, and STOP-BANG scores (P = 0.001). Additionally, binary logistic regression analysis showed that shift work had a significant effect as a risk factor on both PSQI scores (CR = 1.25, 95% CI: 1.16-1.32, P = 0.001) and KSS scores (CR = 1.43, 95% CI: 1.29 - 1.59, P = 0.001). Conclusions: Shift work could increase the amount of sleepiness during work in personnel as the main cause, along with other effective factors, such as personal and occupational characteristics.

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“…In Cognitive Ergonomics, "attention" is treated as a single resource or multiple resources that are utilized during human information processing. Driver inattention can be classified into two main types [4], including distraction and mental fatigue [5]. Attention can be disrupted in a variety of ways.…”

Section: A Driver Distraction and Its Typesmentioning

confidence: 99%

Detection of Driver Cognitive Distraction Using Machine Learning Methods

Misra¹

2023

IEEE Access

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Driver distraction is one of the primary causes of crashes. As a result, there is a great need to continuously observe driver state and provide appropriate interventions to distracted drivers. Cognitive distraction refers to the "look but not see" situations when the drivers' eyes are focused on the forward roadway, but their mind is not. Typically, cognitive distractions can result from fatigue, conversation with a co-passenger, listening to the radio, or other similarly loading secondary tasks that do not necessarily take a driver's eyes off the roadway. This makes it one of the hardest distractions to detect as there are no visible clues of driver distraction. In this study, we have identified features from different sources including eyetracking, physiological, and vehicle kinematics data that are relevant towards the classification of distracted and non-distracted drivers via the analysis of data collected from a driving simulator study involving 40 drivers across multiple driving scenarios. The key classification algorithms implemented include Random Forest, Decision Trees and Support Vector Machines. A reduced feature set including pupil area, pupil vertical and horizontal motion was found to be predictive of driver distraction while maintaining an average accuracy of 90% across various road types. Additionally, the impact of road types on driver behaviour was also identified. The findings of the study has practical application towards the design of driver distraction monitoring systems.

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Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction

Cited by 18 publications

References 63 publications

Physical Workload and Work Fatigue among Tank Car Drivers at Surabaya Integrated Terminal

Physical Workload and Work Fatigue among Tank Car Drivers at Surabaya Integrated Terminal

Shift Work as a Risk Factor for Poor Sleep Quality and Daytime Sleepiness in Khorasan Railway Personnel in Iran

Detection of Driver Cognitive Distraction Using Machine Learning Methods

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