典型驾驶压力源的识别及驾驶员压力水平

IF 2.4 3区 工程技术 Q3 TRANSPORTATION Journal of Transportation Safety & Security Pub Date : 2022-10-12 DOI:10.1080/19439962.2022.2128959
Liu Yang, Yunzhou Song, Zhenxin Hu, Zi-Shuang Wang, X. Li
{"title":"典型驾驶压力源的识别及驾驶员压力水平","authors":"Liu Yang, Yunzhou Song, Zhenxin Hu, Zi-Shuang Wang, X. Li","doi":"10.1080/19439962.2022.2128959","DOIUrl":null,"url":null,"abstract":"Abstract Drivers are adversely affected in decision-making and behavior under excessive stress, thus increasing road crash risks. In this study, the Driver Stress Inventory (DSI) was used to identify typical driving stress scenarios and explore the characteristics of drivers among different stress levels. A total of 1881 drivers took part in the survey. The Precedence Chart was used to rank the importance of driving stressors involved in the scale. K-means cluster was adopted to classify drivers’ stress into three levels, namely low, medium and high-stress. Finally, the Kruskal-Wallis test and Mantel-Haenszel test were employed to analyze the similarities and differences of demographic statistical characteristics under different stress levels. The results of the study indicate that various unexpected scenarios caused by the abnormal behavior of other road users are the most typical stressors. Drivers in the high-stress group tended to be younger and less experienced. Professional drivers reported higher stress than nonprofessional drivers. In addition, high-stress drivers were more prone to be involved in traffic crashes.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Recognition of typical driving stressors and driver stress level in a Chinese sample\",\"authors\":\"Liu Yang, Yunzhou Song, Zhenxin Hu, Zi-Shuang Wang, X. Li\",\"doi\":\"10.1080/19439962.2022.2128959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Drivers are adversely affected in decision-making and behavior under excessive stress, thus increasing road crash risks. In this study, the Driver Stress Inventory (DSI) was used to identify typical driving stress scenarios and explore the characteristics of drivers among different stress levels. A total of 1881 drivers took part in the survey. The Precedence Chart was used to rank the importance of driving stressors involved in the scale. K-means cluster was adopted to classify drivers’ stress into three levels, namely low, medium and high-stress. Finally, the Kruskal-Wallis test and Mantel-Haenszel test were employed to analyze the similarities and differences of demographic statistical characteristics under different stress levels. The results of the study indicate that various unexpected scenarios caused by the abnormal behavior of other road users are the most typical stressors. Drivers in the high-stress group tended to be younger and less experienced. Professional drivers reported higher stress than nonprofessional drivers. In addition, high-stress drivers were more prone to be involved in traffic crashes.\",\"PeriodicalId\":46672,\"journal\":{\"name\":\"Journal of Transportation Safety & Security\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2022-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Transportation Safety & Security\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/19439962.2022.2128959\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Transportation Safety & Security","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/19439962.2022.2128959","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 1

摘要

过度压力会对驾驶员的决策和行为产生不利影响,从而增加道路碰撞风险。本研究采用驾驶员压力量表(DSI)识别典型驾驶压力情景,探讨不同压力水平下驾驶员的特征。共有1881名司机参与了这项调查。优先级图用于对量表中涉及的驾驶压力因素的重要性进行排序。采用K-means聚类将驾驶员的压力分为低、中、高三个等级。最后,采用Kruskal-Wallis检验和Mantel-Haenszel检验分析不同应激水平下人口统计特征的异同。研究结果表明,由其他道路使用者的异常行为引起的各种意外情景是最典型的压力源。高压力组的司机往往更年轻,经验也更少。专业司机比非专业司机的压力更大。此外,压力大的司机更容易发生交通事故。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Recognition of typical driving stressors and driver stress level in a Chinese sample
Abstract Drivers are adversely affected in decision-making and behavior under excessive stress, thus increasing road crash risks. In this study, the Driver Stress Inventory (DSI) was used to identify typical driving stress scenarios and explore the characteristics of drivers among different stress levels. A total of 1881 drivers took part in the survey. The Precedence Chart was used to rank the importance of driving stressors involved in the scale. K-means cluster was adopted to classify drivers’ stress into three levels, namely low, medium and high-stress. Finally, the Kruskal-Wallis test and Mantel-Haenszel test were employed to analyze the similarities and differences of demographic statistical characteristics under different stress levels. The results of the study indicate that various unexpected scenarios caused by the abnormal behavior of other road users are the most typical stressors. Drivers in the high-stress group tended to be younger and less experienced. Professional drivers reported higher stress than nonprofessional drivers. In addition, high-stress drivers were more prone to be involved in traffic crashes.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
期刊最新文献
Examining the crash risk factors associated with cycling by considering spatial and temporal disaggregation of exposure: Findings from four Dutch cities Traffic safety performance evaluation in a connected vehicle environment with queue warning and speed harmonization applications Enhancing bicyclist survival time in fatal crashes: Investigating the impact of faster crash notification time through explainable machine learning Factors affecting pedestrian injury severity in pedestrian-vehicle crashes: Insights from a data mining and mixed logit model approach Prediction of high-risk bus drivers characterized by aggressive driving behavior
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1