FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4291034743> ?p ?o ?g. }

• W4291034743 endingPage "100241" @default.
• W4291034743 startingPage "100241" @default.
• W4291034743 abstract "Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cautiously as a result of risk-compensating behaviour. This endogeneity has been overlooked in the previous driver behaviour studies and may provide new insight into the effects of sleepiness on driving performance. In addition, the Karolinska Sleepiness Scale (KSS) has been widely used to quantify sleepiness. However, the KSS is a subjective self-reported measure and is reliant on honest reporting and understanding of the scale. An alternative way of quantifying sleepiness is using drivers’ heart rate and correlating it with their sleepiness. While recent advances in data collection technologies have made it possible to collect heart rate data in real-time and in an unobtrusive way, their application in measuring sleepiness particularly among truck drivers has been unexplored. This study aims to address these gaps and contribute to analytic methods in road safety research by collecting truck drivers’ heart rate data in real-time, measuring sleepiness from those data, and using it in an instrumental variable modelling framework to investigate its effect on driving performance. To this end, a driving simulator experiment was conducted in Belgium and heart rate data were collected for 35 truck drivers via sensors installed on the steering wheel of the simulator. Additional demographic data were collected using a questionnaire before the experiment. An instrumental variable model consisting of a discrete binary logit and a continuous generalized linear model with grouped random parameters and heterogeneity in their means was then developed to study the effects of driver sleepiness on headway. Results indicate that age, years of holding driver licence, road type, type of truck transport, and weekly distance travelled are significantly associated with sleepiness among the participants of this study. Sleepy driving is associated with reduced headway for 30.5% of the drivers and increased headway for the other 69.5%, and night-time shift is associated with such varied effects. These findings indicate that there may be group- or context-specific risk patterns which cannot be explicitly addressed by hours of service regulations and therefore, transport operators, driver trainers and fleet managers should identify and handle such context-specific high risk patterns in order to ensure safe operations." @default.
• W4291034743 created "2022-08-13" @default.
• W4291034743 creator A5000045480 @default.
• W4291034743 creator A5001373358 @default.
• W4291034743 creator A5002509062 @default.
• W4291034743 creator A5022078886 @default.
• W4291034743 creator A5027062716 @default.
• W4291034743 creator A5031241833 @default.
• W4291034743 creator A5057237572 @default.
• W4291034743 creator A5060224346 @default.
• W4291034743 creator A5062060515 @default.
• W4291034743 creator A5068967962 @default.
• W4291034743 creator A5071601970 @default.
• W4291034743 creator A5074449880 @default.
• W4291034743 creator A5086936577 @default.
• W4291034743 date "2022-12-01" @default.
• W4291034743 modified "2023-10-05" @default.
• W4291034743 title "Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means" @default.
• W4291034743 cites W1529932494 @default.
• W4291034743 cites W1583412610 @default.
• W4291034743 cites W1894717885 @default.
• W4291034743 cites W1954702524 @default.
• W4291034743 cites W1965258823 @default.
• W4291034743 cites W1969074815 @default.
• W4291034743 cites W1975563220 @default.
• W4291034743 cites W1977455965 @default.
• W4291034743 cites W1981638968 @default.
• W4291034743 cites W1986693528 @default.
• W4291034743 cites W1989748291 @default.
• W4291034743 cites W1995453543 @default.
• W4291034743 cites W1995500779 @default.
• W4291034743 cites W1998807199 @default.
• W4291034743 cites W2004916318 @default.
• W4291034743 cites W2017460826 @default.
• W4291034743 cites W2019883359 @default.
• W4291034743 cites W2032816901 @default.
• W4291034743 cites W2057750958 @default.
• W4291034743 cites W2068852650 @default.
• W4291034743 cites W2069010383 @default.
• W4291034743 cites W2071002535 @default.
• W4291034743 cites W2075416197 @default.
• W4291034743 cites W2083767335 @default.
• W4291034743 cites W2105793079 @default.
• W4291034743 cites W2126941961 @default.
• W4291034743 cites W2139830606 @default.
• W4291034743 cites W2140336646 @default.
• W4291034743 cites W2166628043 @default.
• W4291034743 cites W2170131944 @default.
• W4291034743 cites W2291687153 @default.
• W4291034743 cites W2343811890 @default.
• W4291034743 cites W2404972165 @default.
• W4291034743 cites W2537864931 @default.
• W4291034743 cites W2556398113 @default.
• W4291034743 cites W2564863421 @default.
• W4291034743 cites W2607529083 @default.
• W4291034743 cites W2614954172 @default.
• W4291034743 cites W2682367792 @default.
• W4291034743 cites W2750077433 @default.
• W4291034743 cites W2751088292 @default.
• W4291034743 cites W2765174074 @default.
• W4291034743 cites W2791185793 @default.
• W4291034743 cites W2795547633 @default.
• W4291034743 cites W2796352901 @default.
• W4291034743 cites W2884426955 @default.
• W4291034743 cites W2912724100 @default.
• W4291034743 cites W2937494409 @default.
• W4291034743 cites W3004128765 @default.
• W4291034743 cites W3004423606 @default.
• W4291034743 cites W3081839957 @default.
• W4291034743 cites W3131119837 @default.
• W4291034743 cites W3134210446 @default.
• W4291034743 cites W3155547539 @default.
• W4291034743 cites W3165772246 @default.
• W4291034743 cites W3175746181 @default.
• W4291034743 cites W3200954796 @default.
• W4291034743 cites W3204031959 @default.
• W4291034743 cites W4224297459 @default.
• W4291034743 cites W4254625751 @default.
• W4291034743 doi "https://doi.org/10.1016/j.amar.2022.100241" @default.
• W4291034743 hasPublicationYear "2022" @default.
• W4291034743 type Work @default.
• W4291034743 citedByCount "3" @default.
• W4291034743 countsByYear W42910347432023 @default.
• W4291034743 crossrefType "journal-article" @default.
• W4291034743 hasAuthorship W4291034743A5000045480 @default.
• W4291034743 hasAuthorship W4291034743A5001373358 @default.
• W4291034743 hasAuthorship W4291034743A5002509062 @default.
• W4291034743 hasAuthorship W4291034743A5022078886 @default.
• W4291034743 hasAuthorship W4291034743A5027062716 @default.
• W4291034743 hasAuthorship W4291034743A5031241833 @default.
• W4291034743 hasAuthorship W4291034743A5057237572 @default.
• W4291034743 hasAuthorship W4291034743A5060224346 @default.
• W4291034743 hasAuthorship W4291034743A5062060515 @default.
• W4291034743 hasAuthorship W4291034743A5068967962 @default.
• W4291034743 hasAuthorship W4291034743A5071601970 @default.
• W4291034743 hasAuthorship W4291034743A5074449880 @default.
• W4291034743 hasAuthorship W4291034743A5086936577 @default.
• W4291034743 hasBestOaLocation W42910347431 @default.

• next