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

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Amir Pooyan Afghari , Eleonora Papadimitriou , Fran Pilkington-Cheney , Ashleigh Filtness , Tom Brijs , Kris Brijs , Ariane Cuenen , Bart De Vos , Helene Dirix , Veerle Ross , Geert Wets , André Lourenço , Lourenço Rodrigues
{"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","authors":"Amir Pooyan Afghari ,&nbsp;Eleonora Papadimitriou ,&nbsp;Fran Pilkington-Cheney ,&nbsp;Ashleigh Filtness ,&nbsp;Tom Brijs ,&nbsp;Kris Brijs ,&nbsp;Ariane Cuenen ,&nbsp;Bart De Vos ,&nbsp;Helene Dirix ,&nbsp;Veerle Ross ,&nbsp;Geert Wets ,&nbsp;André Lourenço ,&nbsp;Lourenço Rodrigues","doi":"10.1016/j.amar.2022.100241","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p><p>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.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"36 ","pages":"Article 100241"},"PeriodicalIF":12.5000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665722000306/pdfft?md5=368b5f5598c02639562d0c73dc43fd55&pid=1-s2.0-S2213665722000306-main.pdf","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytic Methods in Accident Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213665722000306","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 7

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.

研究卡车司机嗜睡对车头时距的影响:一个具有分组随机参数和均值异质性的工具变量模型
困倦是卡车司机中常见的人为因素,它是由于睡眠不足或白天的时间,导致警觉性、注意力和驾驶性能受损。虽然司机的困倦可能与道路上的风险增加有关,但由于风险补偿行为,困倦的司机可能会更谨慎地驾驶。这种内生性在以前的驾驶员行为研究中被忽视了,这可能为研究困倦对驾驶性能的影响提供了新的见解。此外,Karolinska嗜睡量表(KSS)已被广泛用于量化嗜睡。然而,KSS是一种主观的自我报告测量,依赖于诚实的报告和对量表的理解。另一种量化困倦程度的方法是使用司机的心率,并将其与困倦程度联系起来。虽然数据收集技术的最新进展使得以一种不显眼的方式实时收集心率数据成为可能,但它们在测量卡车司机睡意方面的应用尚未得到探索。本研究旨在通过实时收集卡车司机的心率数据,测量这些数据的困倦程度,并在工具变量建模框架中使用它来研究其对驾驶性能的影响,从而解决这些差距,并为道路安全研究中的分析方法做出贡献。为此,我们在比利时进行了驾驶模拟器实验,通过安装在模拟器方向盘上的传感器采集了35名卡车司机的心率数据。在实验前,通过问卷调查收集了额外的人口统计数据。建立了一个由离散二元logit和连续广义线性模型组成的工具变量模型,该模型具有分组随机参数和均值异质性,用于研究驾驶员睡眠对车头时距的影响。结果表明,年龄、持有驾驶执照的年数、道路类型、卡车运输类型和每周行驶距离与本研究参与者的嗜睡程度显著相关。瞌睡驾驶导致30.5%的司机车头时距减小,69.5%的司机车头时距增大,夜班与这些不同的影响有关。这些发现表明,可能存在特定群体或特定环境的风险模式,这些风险模式无法通过服务时间法规明确解决,因此,运输经营者、驾驶员培训师和车队管理人员应识别和处理此类特定环境的高风险模式,以确保安全运营。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
22.10
自引率
34.10%
发文量
35
审稿时长
24 days
期刊介绍: Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信