3 research outputs found

    Bus Driver Fatigue Final Report

    No full text
    Transport for London (TfL) requested independent research services regarding fatigue in London bus drivers. The research reported here was commissioned by TfL to understand the present situation with regard to fatigue and this report provides a roadmap to investigate solutions and to delve deeper into some of the observations made by the authors. This project sought to understand the extent and nature of fatigue, the contributing factors to fatigue, and what solutions could be implemented to address fatigue. The key components of this report are 1) a targeted literature review focusing on sleepiness and fatigue amongst bus drivers, and a broader review of fatigue prevention strategies, 2) a review of internal policy for managing fatigue, 3) focus groups with bus drivers, 4) interviews with managers, 5) a survey of bus drivers, 6) on-road observation study, and, 7) discussion of potential solutions. </p

    Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means

    No full text
    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

    Developing countermeasures to improve fitness to drive in professional drivers [Abstract]

    No full text
    Professional drivers are at risk to be involved in a crash associated with an impairment (Talbot et al., 2016; Hanowski, et al., 2007). Working conditions, for example shift work, can lead to increased fatigue or stress (e.g. Ă…kerstedt et al., 2001) and alcohol, drugs (illegal/medicinal) increases crash risk (Romano et al., 2014). The PANACEA project (2021-2024), aims to design and pilot a professional driver fitness to drive platform that combines technology measuring alcohol, drugs, fatigue and stress prior to a shift and fatigue during a shift and delivers post-trip countermeasures. This abstract will provide an overview of how the countermeasures addressing fatigue, stress, alcohol use and drugs were developed. The countermeasures will be listed, but results about their effectiveness and user acceptance are not yet available as trials are ongoing.</p
    corecore