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| Seuring, Stefan |
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| Nor Azizi, S. |
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| Pato, Margarida Vaz |
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| Kölker, Katrin |
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| Huber, Oliver |
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| Király, Tamás |
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| Spengler, Thomas Stefan |
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| Al-Ammar, Essam A. |
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| Dargahi, Fatemeh |
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| Mota, Rui |
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| Mazalan, Nurul Aliah Amirah |
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| Macharis, Cathy | Brussels |
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| Arunasari, Yova Tri |
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| Nunez, Alfredo | Delft |
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| Bouhorma, Mohammed |
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| Bonato, Matteo |
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| Fitriani, Ira |
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| Autor Correspondente Coelho, Sílvia. |
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| Pond, Stephen |
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| Okwara, Ukoha Kalu |
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| Toufigh, Vahid |
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| Campisi, Tiziana | Enna |
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| Ermolieva, Tatiana |
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| Sánchez-Cambronero, Santos |
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| Agzamov, Akhror |
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Filtness, Ashleigh
in Cooperation with on an Cooperation-Score of 37%
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Publications (20/20 displayed)
- 2023Framework for behaviour change implemented in real-time and post-trip interventions of the H2020 i-DREAMS naturalistic driving project
- 2023State-of-the-art Technologies for Post-Trip Safety Interventions
- 2023Effectiveness of real-time and post-trip interventions from the H2020 i-DREAMS naturalistic driving project: A Sneak Preview
- 2022Investigating the effects of sleepiness in truck drivers on their headway: an instrumental variable model with grouped random parameters and heterogeneity in their meanscitations
- 2022Methodology for the Evaluation of Safety Interventions
- 2022Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their meanscitations
- 2021Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countriescitations
- 2021The i-DREAMS intervention strategies to reduce driver fatigue and sleepiness for different transport modescitations
- 2021Post-trip safety interventions: State-of-the-art, challenges, and practical implicationscitations
- 2021Modelling driver decision-making at railway level crossings using the abstraction decomposition spacecitations
- 2019Riding the emotional roller-coaster: Using the circumplex model of affect to model motorcycle riders’ emotional state-changes at intersectionscitations
- 2019What do driver educators and young drivers think about driving simulators? A qualitative draw-and-talk studycitations
- 2019Riding the emotional roller-coastercitations
- 2019The effect of psychosocial factors on perceptions of driver education using the goals for driver education frameworkcitations
- 2019Review and ranking of crash risk factors related to the road infrastructurecitations
- 2018A mixed-methods study of driver education informed by the Goals for Driver Education: Do young drivers and educators agree on what was taught?citations
- 2018Serious Road Traffic Injuries in Europe, Lessons from the EU Research Project SafetyCubecitations
- 2018Burden of injury of serious road injuries in six EU countriescitations
- 2016Identification of Road User related Risk Factors. SAfetyCube Deliverable 4.1
- 2016Identification of Road User related Risk Factors. SAfetyCube Deliverable 4.1
Places of action
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article
Investigating the effects of sleepiness in truck drivers on their headway: an instrumental variable model with grouped random parameters and heterogeneity in their means
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 ...
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