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Article(id=1149789602111779262, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768563956826506, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2405683, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1722182400000, receivedDateStr=2024-07-29, revisedDate=1741881600000, revisedDateStr=2025-03-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1752060803200, onlineDateStr=2025-07-09, pubDate=1749312000000, pubDateStr=2025-06-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752060803200, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752060803200, creator=13701087609, updateTime=1752060803200, updator=13701087609, issue=Issue{id=1149768563956826506, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='16', pageStart='6587', pageEnd='7021', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752055787314, creator=13701087609, updateTime=1768456850262, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1218559607937618069, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768563956826506, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1218559607937618070, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768563956826506, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=6942, endPage=6952, ext={EN=ArticleExt(id=1149789602384409023, articleId=1149789602111779262, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Mental Workload of Drivers at Curve Section of Interchange Ramp Based on Natural Driving Data, columnId=1156262728772735295, journalTitle=Science Technology and Engineering, columnName=Papers·Traffics and Transportations, runingTitle=null, highlight=null, articleAbstract=

To investigate the psychological load characteristics of drivers on interchange ramp curves, an naturalistic driving test was conducted with 38 participants in a high-density interchange group in Chongqing. The PhysioLAB physiological monitoring system was used to collect electrocardiogram (ECG) data from drivers navigating the ramp curves. Three typical ramps were taken as research objects, a factor analysis model was established based on heart rate (HR), heart rate increment (HRI), and heart rate variability[root mean square of the difference between adjacent normal cardiac cycles(RMSSD) and standard deviation of RR intervals for all sinus heartbeats(SDNN)] to analyze the variations in psychological load and influencing factors for drivers on different ramp curves. The results indicate that the average heart rate for drivers on various ramp scenarios is found to range from 60 to 120 beats per minute, with heart rate increments between 3% and 15%. Psychological load on right-turn ramps is more dispersed, while on left-turn and circular ramps, it is more concentrated, with higher load observed on circular ramps. Differences in psychological load are noted among drivers of different styles and genders. Left-turn ramps impact female drivers more, and right-turn ramps impact aggressive drivers more. Psychological load decreases with increased familiarity and increases with larger turning angles. More ramp lanes lead to more vehicle weaving, increasing psychological load. A negative correlation is observed between psychological load and the radius of right-turn ramps, with no linear relationship for left-turn and circular ramps. Driving on consecutive circular ramps increases psychological load.

, correspAuthors=Jin XU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yan-ling LIU, Chong CHENG, Shi ZHANG, Xing-lin ZHU, Jin XU), CN=ArticleExt(id=1149789626166113265, articleId=1149789602111779262, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于实车驾驶数据的互通立交匝道曲线段驾驶负荷研究, columnId=1156262730664366426, journalTitle=科学技术与工程, columnName=论文·交通运输, runingTitle=null, highlight=null, articleAbstract=

为研究互通立交匝道曲线段驾驶人心理负荷特性,在重庆市一处高密度互通立交群开展了38名被试的实车驾驶试验,使用PhysioLAB生理仪采集驾驶人在立交匝道曲线段的心电数据。以3种典型匝道作为研究对象,基于心率HR、心率增长率HRI和心率变异性[相邻正常心动周期差值的均方根(RMSSD)、全部窦性心搏RR间期的标准差(SDNN)]指标,建立因子分析模型,分析驾驶人在不同形式匝道曲线段的心理负荷变化规律以及影响因素。结果表明:驾驶人在不同匝道场景下的心率均值集中在60~120 次/min,心率增长率在3%~15%。驾驶人在右转匝道的心理负荷分布较分散,左转和环形匝道的心理负荷值分布较集中,环形匝道下心理负荷更高;不同驾驶风格和性别驾驶人的心理负荷存在差异,左转匝道对女性驾驶人影响更大,右转匝道对愤怒型驾驶人影响更大;心理负荷随道路环境熟悉程度增加而减小,随转弯角度增大而增大;匝道车道数增加导致车辆交织频繁,心理负荷增加。驾驶人心理负荷与右转匝道半径呈负相关,与左转匝道与环形匝道无线性关系,连续环形匝道行驶会增加心理负荷。

, correspAuthors=徐进, authorNote=null, correspAuthorsNote=
* 徐进(1977—),男,汉族,吉林四平人,博士,教授,博士研究生导师。研究方向:道路安全性设计、车路协同与驾驶行为。E-mail:。
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刘妍伶(2001—),女,汉族,重庆长寿人,硕士研究生。研究方向:综合交通运输与规划管理。E-mail:。

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刘妍伶(2001—),女,汉族,重庆长寿人,硕士研究生。研究方向:综合交通运输与规划管理。E-mail:。

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刘妍伶(2001—),女,汉族,重庆长寿人,硕士研究生。研究方向:综合交通运输与规划管理。E-mail:。

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Transportation System Engineering and Information, 2020(3): 212-218., articleTitle=Analysis of driver mental load on helical ramps and helical bridges based on naturalistic driving data, refAbstract=null), Reference(id=1177977335690182688, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789602111779262, doi=null, pmid=null, pmcid=null, year=2014, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[20], rfOrder=33, authorNames=孙龙, journalName=多维度驾驶风格量表的修订及其与驾驶员情绪状态之间的关系研究, refType=null, unstructuredReference=孙龙. 多维度驾驶风格量表的修订及其与驾驶员情绪状态之间的关系研究[D]. 大连: 辽宁师范大学, 2014., articleTitle=null, refAbstract=null), Reference(id=1177977335753097249, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789602111779262, doi=null, pmid=null, pmcid=null, year=2014, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[20], rfOrder=34, authorNames=Sun Long, journalName=Revision of the multidimensional driving style scale and its relationship with driver emotional states, refType=null, unstructuredReference=Sun Long. Revision of the multidimensional driving style scale and its relationship with driver emotional states[D] Dalian: Liaoning Normal University, 2014., articleTitle=null, refAbstract=null), Reference(id=1177977335807623202, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789602111779262, doi=null, pmid=null, pmcid=null, year=2021, volume=null, issue=1, pageStart=167, pageEnd=176, url=null, language=null, rfNumber=[21], rfOrder=35, authorNames=黄晶, 杨梦婷, journalName=中国公路学报, refType=null, unstructuredReference=黄晶, 杨梦婷. 考虑初始情绪的个性化驾驶负荷状态评价[J]. 中国公路学报, 2021(1): 167-176., articleTitle=考虑初始情绪的个性化驾驶负荷状态评价, refAbstract=null), Reference(id=1177977335862149155, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789602111779262, doi=null, pmid=null, pmcid=null, year=2021, volume=null, issue=1, pageStart=167, pageEnd=176, url=null, language=null, rfNumber=[21], rfOrder=36, authorNames=Huang Jing, Yang Mengting, journalName=Chinese Journal of Highways, refType=null, unstructuredReference=Huang Jing, Yang Mengting. Initial emotion-based evaluation of the personalize driving load state[J]. 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r为匝道半径

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figureFileBig=VmAyIbkZI8ecgqZpaJTDNg==, tableContent=null), ArticleFig(id=1177977333026800630, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789602111779262, language=EN, label=Table 1, caption=

Related information of the research object

, figureFileSmall=null, figureFileBig=null, tableContent=
立交名称 立交型式 匝道形式 编号 匝道限速/
(km·h-1)		 转弯角度/
(°)		 匝道车道数 匝道半径/m
白杨沟立交 变形苜蓿叶形(对称双环式) 右转定向式 BY 40 90 2 110
左转半定向式 BZ 40 270 1 115
石港立交 变形苜蓿叶形(单环式) 右转定向式 SY 40 90 1 115
左转半定向式 SZ 40 270 2 108
腾龙立交 变形苜蓿叶形(单环式) 右转定向式 TY 40 90 2 280
左转半定向式 TZ 40 90 2 87
东环立交 完全苜蓿叶形 环形环圈式 DX1 30 360 2 70
环形环圈式 DX2 30 360 2 47
何家梁立交 完全苜蓿叶形 环形环圈式 HX1 30 360 1 38
环形环圈式 HX2 30 360 1 40
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研究对象的相关信息

, figureFileSmall=null, figureFileBig=null, tableContent=
立交名称 立交型式 匝道形式 编号 匝道限速/
(km·h-1)		 转弯角度/
(°)		 匝道车道数 匝道半径/m
白杨沟立交 变形苜蓿叶形(对称双环式) 右转定向式 BY 40 90 2 110
左转半定向式 BZ 40 270 1 115
石港立交 变形苜蓿叶形(单环式) 右转定向式 SY 40 90 1 115
左转半定向式 SZ 40 270 2 108
腾龙立交 变形苜蓿叶形(单环式) 右转定向式 TY 40 90 2 280
左转半定向式 TZ 40 90 2 87
东环立交 完全苜蓿叶形 环形环圈式 DX1 30 360 2 70
环形环圈式 DX2 30 360 2 47
何家梁立交 完全苜蓿叶形 环形环圈式 HX1 30 360 1 38
环形环圈式 HX2 30 360 1 40
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KMO and Bartlett test results

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KMO取样适切性量数 0.684
巴特利特球形度检验 近似卡方 1 322.992
自由度 6
显著性 0.000
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KMO和巴特利特检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
KMO取样适切性量数 0.684
巴特利特球形度检验 近似卡方 1 322.992
自由度 6
显著性 0.000
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Total variance explanation

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成分 初始特征值 提取载荷平方和 旋转载荷平方和
总计 方差百分比/% 累积/% 总计 方差百分比/% 累积/% 总计 方差百分比/% 累积/%
1 2.543 63.586 63.586 2.543 63.586 63.586 1.745 43.622 43.622
2 0.922 23.045 86.631 0.922 23.045 86.631 1.720 43.009 86.631
3 0.326 8.151 94.782
4 0.209 5.218 100.000
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总方差解释

, figureFileSmall=null, figureFileBig=null, tableContent=
成分 初始特征值 提取载荷平方和 旋转载荷平方和
总计 方差百分比/% 累积/% 总计 方差百分比/% 累积/% 总计 方差百分比/% 累积/%
1 2.543 63.586 63.586 2.543 63.586 63.586 1.745 43.622 43.622
2 0.922 23.045 86.631 0.922 23.045 86.631 1.720 43.009 86.631
3 0.326 8.151 94.782
4 0.209 5.218 100.000
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Component score coefficient matrix

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指标 成分1 成分2
Zscore(HR) -0.330 0.704
Zscore(HRI) -0.697 0.334
Zscore(DSDNN) 0.324 0.187
Zscore(DRMSSD) 0.184 0.319
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成分得分系数矩阵

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指标 成分1 成分2
Zscore(HR) -0.330 0.704
Zscore(HRI) -0.697 0.334
Zscore(DSDNN) 0.324 0.187
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基于实车驾驶数据的互通立交匝道曲线段驾驶负荷研究
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刘妍伶 1 , 程冲 2 , 张诗 1 , 朱兴林 3 , 徐进 1, *
科学技术与工程 | 论文·交通运输 2025,25(16): 6942-6952
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科学技术与工程 | 论文·交通运输 2025, 25(16): 6942-6952
基于实车驾驶数据的互通立交匝道曲线段驾驶负荷研究
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刘妍伶1 , 程冲2, 张诗1, 朱兴林3, 徐进1, *
作者信息
  • 1 重庆交通大学交通运输学院, 重庆 400074
  • 2 中交第二公路勘察设计研究院有限公司, 武汉 430058
  • 3 新疆农业大学交通与物流工程学院, 乌鲁木齐 830052

    • 刘妍伶(2001—),女,汉族,重庆长寿人,硕士研究生。研究方向:综合交通运输与规划管理。E-mail:。

通讯作者:

* 徐进(1977—),男,汉族,吉林四平人,博士,教授,博士研究生导师。研究方向:道路安全性设计、车路协同与驾驶行为。E-mail:。
Mental Workload of Drivers at Curve Section of Interchange Ramp Based on Natural Driving Data
Yan-ling LIU1 , Chong CHENG2, Shi ZHANG1, Xing-lin ZHU3, Jin XU1, *
Affiliations
  • 1 School of Transportation, Chongqing Jiaotong University, Chongqing 400074, China
  • 2 CCCC Second Highway Survey and Design Research Institute Co. , Ltd. , Wuhan 430058, China
  • 3 Xinjiang Agricultural University, Urumqi 830052, China
出版时间: 2025-06-08 doi: 10.12404/j.issn.1671-1815.2405683
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摘要
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为研究互通立交匝道曲线段驾驶人心理负荷特性,在重庆市一处高密度互通立交群开展了38名被试的实车驾驶试验,使用PhysioLAB生理仪采集驾驶人在立交匝道曲线段的心电数据。以3种典型匝道作为研究对象,基于心率HR、心率增长率HRI和心率变异性[相邻正常心动周期差值的均方根(RMSSD)、全部窦性心搏RR间期的标准差(SDNN)]指标,建立因子分析模型,分析驾驶人在不同形式匝道曲线段的心理负荷变化规律以及影响因素。结果表明:驾驶人在不同匝道场景下的心率均值集中在60~120 次/min,心率增长率在3%~15%。驾驶人在右转匝道的心理负荷分布较分散,左转和环形匝道的心理负荷值分布较集中,环形匝道下心理负荷更高;不同驾驶风格和性别驾驶人的心理负荷存在差异,左转匝道对女性驾驶人影响更大,右转匝道对愤怒型驾驶人影响更大;心理负荷随道路环境熟悉程度增加而减小,随转弯角度增大而增大;匝道车道数增加导致车辆交织频繁,心理负荷增加。驾驶人心理负荷与右转匝道半径呈负相关,与左转匝道与环形匝道无线性关系,连续环形匝道行驶会增加心理负荷。

交通工程  /  互通立交  /  匝道  /  心电((ECG)数据  /  心理负荷  /  自然驾驶数据

To investigate the psychological load characteristics of drivers on interchange ramp curves, an naturalistic driving test was conducted with 38 participants in a high-density interchange group in Chongqing. The PhysioLAB physiological monitoring system was used to collect electrocardiogram (ECG) data from drivers navigating the ramp curves. Three typical ramps were taken as research objects, a factor analysis model was established based on heart rate (HR), heart rate increment (HRI), and heart rate variability[root mean square of the difference between adjacent normal cardiac cycles(RMSSD) and standard deviation of RR intervals for all sinus heartbeats(SDNN)] to analyze the variations in psychological load and influencing factors for drivers on different ramp curves. The results indicate that the average heart rate for drivers on various ramp scenarios is found to range from 60 to 120 beats per minute, with heart rate increments between 3% and 15%. Psychological load on right-turn ramps is more dispersed, while on left-turn and circular ramps, it is more concentrated, with higher load observed on circular ramps. Differences in psychological load are noted among drivers of different styles and genders. Left-turn ramps impact female drivers more, and right-turn ramps impact aggressive drivers more. Psychological load decreases with increased familiarity and increases with larger turning angles. More ramp lanes lead to more vehicle weaving, increasing psychological load. A negative correlation is observed between psychological load and the radius of right-turn ramps, with no linear relationship for left-turn and circular ramps. Driving on consecutive circular ramps increases psychological load.

traffic engineering  /  interchange  /  ramp  /  electrocardiogram (ECG) data  /  psychological load  /  natural driving data
刘妍伶, 程冲, 张诗, 朱兴林, 徐进. 基于实车驾驶数据的互通立交匝道曲线段驾驶负荷研究. 科学技术与工程, 2025 , 25 (16) : 6942 -6952 . DOI: 10.12404/j.issn.1671-1815.2405683
Yan-ling LIU, Chong CHENG, Shi ZHANG, Xing-lin ZHU, Jin XU. Mental Workload of Drivers at Curve Section of Interchange Ramp Based on Natural Driving Data[J]. Science Technology and Engineering, 2025 , 25 (16) : 6942 -6952 . DOI: 10.12404/j.issn.1671-1815.2405683
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互通立交匝道是影响立交通行效率的关键路段,具有曲线比例高、曲线变化大的特点,其道路线形、交通状况和行车环境的复杂程度远高于基本路段,是高速公路、快速路互通立交事故的高发区。驾驶人在匝道曲线段行驶过程中需时刻注意道路环境变化,调整车辆转向以适应曲线离心力的变化,导致驾驶人精神高度集中,增加匝道段行驶的事故风险。因此探究驾驶人在不同形式匝道曲线段的心理负荷变化规律,对互通立交的设计优化、交通管理以及事故预防等具有重要意义。
近年来,中外学者对匝道区域的交通流和交通安全开展了大量研究。Spiliopoulou等[1]提出了重新引导和调控匝道内车辆的路线分流措施,并通过宏观仿真验证了该策略在不同交通场景下的有效性。徐进等[2]通过实车驾驶试验分析了右转直连式匝道的汽车运行状态和驾驶行为特征,结果表明,出口匝道中存在一部分驾驶人的减速距离超过减速车道的长度,且横向加速度分布较为分散。张晓波等[3]开展实车试验,采集完全苜蓿叶形和部分苜蓿叶形立交上车辆横向加速度数据,分析了横向加速度的幅值水平、变化趋势和模式,发现部分苜蓿叶形立交环形匝道的横向舒适性较差。张敏等[4]分析实际案例的出口环圈匝道车辆速度和轨迹数据,发现车辆在匝道内的速度与轨迹呈现一致性,后车在跟驰状态下进入匝道易发生追尾事故。Gu等[5]基于无人机视频数据,提出了多级随机参数逻辑回归模型,研究结果表明合流速度、换道能力及合流位置对事故风险有显著影响。张玉等[6]在南山立交与江南立交的4条迂回式匝道开展实车试验,分析了变速阶段纵向加速度的统计分布特征,发现速度变化模式分为入弯减速、稳定行驶和出弯加速3个阶段。王思棋等[7]利用CarSim软件和TruckSim软件进行模拟仿真,研究了小半径匝道上车辆侧滑和侧翻的影响因素,结果表明,车速增加会导致侧滑加剧,且轮胎载荷转移率随道路超高和路面附着系数的增大而减小。潘兵宏等[8]分析不同主线设计速度下环圈出口匝道出口处车辆运行速度,提出了立交匝道过渡段长度最小建议值和纵坡修正系数。陈正欢等[9-10]通过自然驾驶试验采集了螺旋匝道和迂回式匝道内的车速、加速度等数据,发现螺旋式匝道内驾驶人的危险驾驶行为与横向加速度呈正相关,而迂回式匝道内运行速度的离散性随道路曲率半径增大而增大。陈正欢等[10]还采用快速路模拟实验发现在匝道出入口处,环境信息的增加使驾驶人视觉搜索行为复杂化,需要更集中的注意力以识别道路标志和信息。
心电信号作为驾驶人心理活动的关键表征指标,具有较高的敏感度。中外学者在心理负荷研究领域已开展大量的研究。易刚等[11]使用Physiolab生理检测仪连续采集驾驶人在高密度互通立交行驶过程中的心电数据,选择心率变异率指标相邻正常心动周期差值的均方根(RMSSD)、归一化低频功率(LFnorm)、归一化高频功率(HFnorm)、低频与高频比值(LF/HF)以及心率突变率指标HRMR(heat rate mutation rate)建立评价模型。徐进等[12]在山区高速公路上开展大量实车试验,研究了道路设计、车辆运行状态、驾驶人心电信号等与驾驶人心理负荷之间的关系,并建立了相关模型。Yang等[13]通过测量不同标牌场景下的α波段(8~13 Hz)绝对功率,分析了多板和单板标志对驾驶员脑力负荷的影响,发现多板标志下驾驶员前部区域的α波段功率较低,导致驾驶性能下降(减速度增大)。Wei等[14]结合生理信号[心电(electrocardiogram,ECG)和皮肤电(electrodermal activity,EDA)]、交通流和环境等多因素,采用量化分析方法对驾驶人心理负荷进行分类。朱彤等[15]根据心率和心率变异性的变化构建了驾驶人心理负荷的量化模型,发现心率变异性指标具有更高的有效性和可靠度。Di等[16]基于20名年轻驾驶人的实车实验,采用脑电图、眼动追踪技术和主观评价指标,发现道路类型和交通流量对驾驶行为有显著影响。阎莹等[17]建立因子分析与熵值法相结合的驾驶负荷量化模型,发现较大的侧向宽度可以减少驾驶负荷。王畅等[18]开展实车试验采集30名被试视觉分心数据,分析驾驶人执行次任务时视觉分心特性,发现增加车速,视觉分心总时长、平均视觉分心时长、最长视觉分心时长均显著减小。徐进等[19]采集了驾驶人在自然驾驶习惯下经过螺旋形立交匝道段的心电信号,发现车速和匝道曲率对驾驶人心理负荷产生较大影响。
综上可知,中外学者对匝道区域交通流特性开展了大量研究,但鲜有涉及驾驶人在立交匝道曲线段的驾驶负荷分析,缺乏该场景下驾驶人心理负荷的研究。因此,以互通立交匝道曲线段为研究对象,通过实车驾驶试验采集驾驶人在匝道曲线区段行驶的心电信号数据,建立因子分析模型,综合分析驾驶人在不同形式匝道段的心理负荷变化规律,并探究匝道类型、驾驶人个体差异、环境熟悉度、匝道线形等因素对驾驶人心理负荷的影响。研究结果为匝道区域交通安全设施设计以及交通组织方案优化提供理论支撑。
1 实车驾驶试验设计
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1.1 试验地点
通过开展实车驾驶试验来获取驾驶人在自然驾驶状态下的心率数据、轨迹数据,选择重庆市江北区东环立交-石港立交-腾龙立交段作为试验地点,选择5座互通立交中10条匝道区域作为研究对象,将其分为右转定向式匝道、左转半定向式匝道、环形环圈式匝道3种常见的匝道形式,其研究对象如图1所示。匝道区域的相关信息如表1所示,研究范围为匝道曲线行驶路段。
1.2 试验仪器与设备
本试验采用PhysioLAB生理检测仪采集心电(ECG)数据,该仪器通过在被试胸前粘贴心电图贴片,并通过蓝牙连接到计算机采集数据,其采样频率为500 Hz。本试验采用别克GL8作为试验车辆,并在试验车辆前后玻璃各安装行车记录仪,记录整个行车过程及道路相关信息。试验仪器和车辆如图2所示。
1.3 被试
共38名驾驶人参与本次实车试验,由于第28位驾驶人试验途中设备连接中断导致数据缺失,最终可供使用的有效数据为37名驾驶人。其中男性驾驶人21名,女性驾驶人16名,年龄分布为27~53岁,平均年龄在40.2岁,实际驾龄在6~20年,平均实际驾龄为12.5年,驾驶里程均大于5×104 km。所有被试需填写中文版多维度驾驶风格量表(MDSI-C)[20],根据填写结果,将37位驾驶人划分为15位冒险型驾驶人、11位愤怒型驾驶人、11位焦虑型驾驶人。所有被试对试验路段并不熟悉,近一个月内未在此路段驾驶。所有被试要求在试验前不饮酒,保持良好的休息。其被试者信息统计如图3所示。
1.4 试验流程与数据处理
本次试验共开展13 d,为避免高峰时期车流量大出现拥堵和夜晚光线较暗影响驾驶人心理负荷,选择工作日晴天/阴天的09:30—11:30、13:00—17:00进行。试验开始前,为驾驶人佩戴心率仪器,让被试(驾驶人)按照个人的驾驶习惯沿试验路线行驶1、2个循环,中途间隔10 min休息,尽可能避免驾驶人疲劳驾驶,保证采集到自由流状态下驾驶人的心理数据,并采用3倍标准差法去除异常数据,保证心率数据的有效性。
1.5 心电指标选取
选择心率(HR)指标、心率增长率(HRI)指标以及心率变异性(HRV)指标:相邻正常心动周期差值的均方根(RMSSD)和全部窦性心搏RR间期的标准差(SDNN)作为评价指标。其中HR、HRI指标能客观地反映驾驶人在该路段驾驶过程中的平均心理紧张程度,HR、HRI值越大,驾驶人心理负荷越大。RMSSD和SDNN能反映个体的迷走神经状态。RMSSD、SDNN值越小,驾驶人心理负荷越大。指标的计算公式为
HR= 60 R - R
HRI= H R i - H R m i n H R m i n
RMSSD= 1 N - 1 ∑ i = 1 N - 1 ( R R i + 1 - R R i ) 2
SDNN= 1 N ∑ i = 1 N ( R R i - R R ¯ ) 2
式中:R-R为RR间期,即相邻R波的时间差,ms;HRi为驾驶人的瞬时心率值(次/分钟);HRmin为试验中驾驶人去除异常心率值后的最小值(次/min);N为一次完整试验中R波总数;RRi+1为第i+1个RR间期值;RRi为第i个RR间期值; R R ¯为一次完整试验中所有RR间期序列的平均值。
2 驾驶人心理特征分析
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驾驶人的心理负荷状态对道路交通安全有显著影响。心电信号是反映驾驶人心理负荷变化的有效且直接的评价指标,研究表明,驾驶人在静息情绪、愤怒情绪、暴躁情绪等不同情绪状态下,心电信号有不同变化[21]。为得到驾驶人在匝道曲线段的心理特征,截取驾驶人在匝道曲线段的心电数据,以石港左转匝道为例,其匝道曲线段数据截取起止点如图4(a)所示,绘制驾驶人心率(HR)变化曲线如图4(b)所示。
由图4(b)可知,驾驶人心率(HR)波动较大,变化趋势不明显。为得到良好的变化趋势,选择心电数据特征值进行后续分析。汇总37位驾驶人在不同匝道下的心理特征变化,包括心率均值HR、心率增长率均值HRI、心率变异率SDNN及RMSSD指标。将匝道的平曲线半径按照从小到大的方式排列,绘制驾驶人在不同场景下心理特征的变化趋势图,如图5所示。
由图5(a)可知,驾驶人在不同匝道场景下的心率均值差异较小,集中在60~120 次/min。除白杨沟左转匝道外,其余右转和左转匝道随着半径增大,心率均值降低,表明半径越小,心理负荷越高。环形匝道中心率均值存在波动,峰值点为HX2与DX1匝道。
由图5(b)可知,驾驶人在不同匝道的心率增长率在3%~15%,左转匝道的心率增长率高于右转和环形匝道。右转匝道下,心率增长率随半径增加而降低。左转匝道中,石港左转匝道的心率增长率最大,因其长度较长且路面不平整,导致驾驶人紧张情绪增加。环形匝道的心率增长率变化趋势与心率均值相似。
由图5(c)、图5(d)可知,驾驶人的心率变异性指标SDNN和RMSSD变化趋势大致相同。右转匝道下,SDNN和RMSSD与平曲线半径呈正相关,半径越大,心理负荷越小。左转匝道中,腾龙左转匝道的心率变异率指标较低,因其途径短隧道,驾驶人视线变暗,心理负荷增大。环形匝道下,心率变异性指标变化趋势与心率均值和心率增长率的变化趋势相反,表明何家梁环形匝道2和东环环形匝道1的心理负荷较大。
3 心理负荷模型构建及综合评价
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3.1 因子分析模型
由图6可知,心率指标HR与心率变异性RMSSD指标呈现负相关,心率指标HR与心率变异性SDNN指标呈负相关,心率变异性RMSSD指标与心率变异性SDNN指标呈正相关。
由图6可知,4个指标之间存在一定相关性,两两指标之间相关程度有所差异,每个指标均可表征驾驶人心理负荷强度。但单个指标存在局限性,因此可利用因子分析方法提取4个指标的共性指标,提出评价驾驶人心理负荷的综合指标。
对变量作KMO(Kaiser-Meyer-Olkin)检验和Bartlett球形检验,检验结果如表2所示。KMO取样适度测量值为0.684 >0.6,即变量适用因子分析。
表3为总方差解释表,根据特征值大于1或累积方差贡献率大于80%的原则确定主成分个数[17]。旋转后的主成分1和主成分2的特征值分别为1.745和1.720,均大于1的阈值;旋转后的荷载平方和,提取主成分1和主成分2的累积方差贡献率为86.631%,满足累积方差贡献率大于80%的要求。综上,确定提取2个公因子。
根据表4可知,主成分F1、主成分F2的表达式,由式(5)可知,心率变异率时域指标RMSSD、SDNN为关键指标,F1的值越大,驾驶人心理负荷越小,由式(6)可知,心率HR和心率增长率HRI为关键指标,F2的值越大,驾驶人心理负荷越大。
F1=-0.330ZHR-0.697ZHRI+0.324ZDSDNN+0.184ZDRMSSD
F2=-0.704ZHR+0.334ZHRI+0.187ZDSDNN+0.319ZDRMSSD
式中:ZHR、ZHRI、ZDSDNN、ZDRMSSD为使用标准化处理后各心电指标。
得出心理负荷的综合评价量化模型计算公式为
H= 43.622 86.631F1+ 43.009 86.631F2
3.2 驾驶人心理负荷综合评价
根据因子分析后的心理负荷综合评价量化模型式(7),按照3种不同形式匝道分别对驾驶人的心理负荷综合量化值进行计算统计,绘制驾驶人心理负荷箱型图与相对频率与累计频率分布曲线图,如图7所示。
由图7(a)可知,驾驶人在环形匝道行驶时心理负荷最大,右转匝道行驶时心理负荷最小,且环形匝道行驶过程中心理负荷量化值分布更集中。图7(b)为3种不同形式匝道驾驶人心理负荷值的相对频率和累计频率曲线。右转定向式匝道下心理负荷综合量化值H集中在0.1~0.3,相对频率峰值最小,表明驾驶人在右转匝道心理负荷最小。左转半定向式和环形环圈式匝道下心理负荷值集中在0.3~0.5,累计频率曲线在H为0.6时出现分叉点,低负荷区(H<0.6)左转半定向式匝道的心理负荷值累计频率大于环形环圈式匝道,高负荷区(H>0.6)环形环圈式匝道的心理负荷值累计占比更高,表明驾驶人在环形环圈式匝道下心理负荷更高。
4 驾驶人心理负荷影响因素
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4.1 驾驶人个体影响
驾驶人心理负荷受性别、驾驶风格、道路环境和车辆状况等外界因素影响。统计37位驾驶人在10条匝道区域下的心理负荷综合量化值并绘制热力图,如图8所示。可以看出,驾驶人个体差异导致心理负荷差异较大。部分驾驶人在所有匝道区域下心理负荷较高,部分仅在个别区域心理负荷较高,而熟练驾驶人在所有区域心理负荷稳定。因此,按驾驶风格和性别分类,分析个体差异对心理负荷的影响。
根据驾驶人性别不同,对所有驾驶人心理负荷综合量化值进行分类汇总,以性别作为检验变量进行方差分析(右转匝道:F=0.027,P=0.89>0.05;左转匝道:F=0.31,P=0.57>0.05;环形匝道:F=0.000,P=0.988>0.05),结果表明,不同性别驾驶人的心理负荷值无统计学上的显著差异。绘制驾驶人在3种不同形式匝道下驾驶人心理负荷分布图,如图9所示。
由图9(a)可知,在右转匝道下,女性驾驶人心理负荷的均值与男性驾驶人差异不大,但在左转匝道以及环形匝道下,女性驾驶人的心理负荷值均明显大于男性驾驶人。由图9(b)可知,男性驾驶人在匝道区域中心理负荷分布相对集中,而女性驾驶人心理负荷分布相对分散。表明整体上女性驾驶人的心理负荷更高。
根据驾驶人驾驶风格不同,对所有驾驶人心理负荷综合量化值进行分类汇总,以驾驶风格作为检验变量进行方差分析(右转匝道:F=5.63,P=0.059>0.05;左转匝道:F=0.277,P=0.87>0.05;环形匝道:F=1.04,P=0.59>0.05;),结果表明,不同驾驶风格驾驶人的心理负荷值无统计学上的显著差异。根据驾驶人驾驶风格不同,对所有驾驶人心理负荷综合量化值进行分类汇总,绘制驾驶人3种不同形式匝道下驾驶人心理负荷分布图,如图10所示。
由图10(a)可知,在右转匝道和环形匝道场景下,愤怒型驾驶人的心理负荷值高于焦虑型和冒险型驾驶人,且右转弯驾驶人驾驶风格表现更为显著,在左转匝道场景下,3种驾驶风格驾驶人心理负荷均值差异不大。由图10(b)可知,焦虑性和愤怒型驾驶人心理负荷值较为集中,而冒险型驾驶人心理负荷值较为分散。焦虑型驾驶人心理负荷值集中在0.3~0.5,愤怒型驾驶人心理负荷值集中在0.2~0.4。根据累计频率85%分位值可知,愤怒型驾驶人心理负荷值高于冒险型驾驶人,焦虑型驾驶人85%分位值最低。表明愤怒型驾驶人在匝道曲线段行驶时心理负荷最大。
4.2 环境熟悉程度
对行驶环境的熟悉程度会影响到驾驶人的判断和决策行为,降低驾驶任务的复杂性和难度,进而改变驾驶人的心理负荷。根据驾驶人对环境的熟悉程度不同,对所有驾驶人心理负荷综合量化值进行分类汇总,以环境熟悉度作为检验变量进行方差分析(右转匝道:F=9.99,P=0.001<0.05;左转匝道:F=1.24,P=0.26>0.05;环形匝道:F=9.97,P=0.001<0.05;),分析结果表明在不同的环境熟悉度下,驾驶人的心理负荷在右转匝道和环形匝道具有统计学上的显著差异,在左转匝道处无统计学上的显著差异。
绘制驾驶人在右转定向式匝道、左转半定向式匝道和环形环圈式匝道3种不同形式匝道下驾驶人心理负荷量化值分布图,如图11所示。可以看出,驾驶人在3种不同形式的匝道场景下,对行驶路径不熟悉的驾驶人的心理负荷明显高于对行驶路径熟悉的驾驶人,表明驾驶人对道路环境越熟悉,其精神负荷越小。
4.3 匝道线形
匝道线形是影响驾驶人驾驶行为的重要指标。过小的匝道半径与转弯角度会增加驾驶人侧向驾驶压力,导致驾驶负荷上升。匝道车道数的增加会使得车辆交织行为更为复杂,增加驾驶负荷。
根据转弯角度、匝道车道数和曲线半径的不同,对所有驾驶人的心理负荷综合量化值进行分类汇总。以转弯角度为检验变量进行方差分析(F=7.06,P=0.029<0.05),表明不同转弯角度下心理负荷具有统计学上的显著差异。以匝道车道数为检验变量进行方差分析(右转匝道:F=22.89,P=0.000<0.05;左转匝道:F=4.722,P=0.029<0.05;环形匝道:F=1.987,P=0.158>0.05),表明不同匝道车道数下,右转和左转匝道的心理负荷具有统计学上的显著差异,环形匝道无显著差异。以曲线半径为检验变量进行方差分析(右转匝道:F=23.32,P=0.000<0.05;左转匝道:F=5.58,P=0.061>0.05;环形匝道:F=7.00,P=0.071>0.05),表明不同曲线半径下,右转匝道的心理负荷具有统计学上的显著差异,左转和环形匝道无显著差异。现绘制不同转弯角度、不同匝道车道数以及不同匝道半径驾驶人心理负荷分布图,如图12所示。
图12(a)为不同转弯角度下驾驶人心理负荷综合量化值箱型图。可以看出,驾驶人在转角为90°时平均心理负荷最小,并且随着转弯角度的增加,驾驶人心理负荷增大。图12(b)为不同匝道车道数下驾驶人的心理负荷图,由图可知,匝道车道数为2的场景下,驾驶人有着更高的心理负荷值。车道数增加,道路变宽,驾驶人有更高的操作空间,但结合行车记录仪视频可知,匝道车道数为两车道时,车辆间存在并行、超车等行为,驾驶人需时刻注意侧方和后方来车,增加驾驶负荷。车流量大时,车辆交织现象更复杂,驾驶人在两车道匝道行驶心理负荷更大。
如图12(c)所示,右转匝道下,半径值越大,驾驶人心理负荷越小,即驾驶人越不紧张;左转匝道下,驾驶人心理负荷随着匝道半径变化存在着先升后降的趋势,在石港左转匝道处驾驶负荷最大,结合实际场景分析发现SZ匝道路面不平整,路况条件极差,驾驶人需集中精力注意路面坑洼,导致心理负荷增大;而在环形匝道处,由于半径值差异较小,导致驾驶人心理负荷值存在波动现象。在何家梁环形匝道处,半径值越大,驾驶人心理负荷越高,在东环环形匝道处,半径值越大,驾驶人心理负荷越低。分析发现后行驶的环形匝道区域DX2和HX2的心理负荷值均大于DX1和HX1,说明连续的环形匝道行驶会增加驾驶人的驾驶负荷。
5 结论
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采集驾驶人在不同形式匝道(右转匝道、左转匝道、环形匝道)曲线段行驶的心电数据,分析驾驶人的心理负荷。并基于心率(HR)、心率增长率(HRI)和心率变异性(RMSSD、SDNN)指标,采用因子分析法综合量化心理负荷,对比不同形式匝道的心理负荷差异,并分析驾驶人性别、驾驶风格、环境熟悉度及匝道线形对心理负荷的影响,得出如下结论。
(1)驾驶人在不同匝道场景下的心率均值集中在60~120次/min,心率增长率在3%~15%,左转匝道的心率增长率高于右转和环形匝道。心率变异性指标SDNN和RMSSD在右转匝道中随曲线半径增大而增大,心理负荷减小。
(2)右转定向式匝道区域的心理负荷值分布较分散,左转半定向式和环形环圈式匝道区域的心理负荷值分布较集中。环形环圈式匝道在高负荷区(H>0.6)的心理负荷值累计占比更高,表明驾驶人在该匝道下心理负荷更高。
(3)立交匝道曲线段行驶中,驾驶人心理负荷受个体差异影响较大,不同匝道形式对驾驶人的影响存在差异。女性驾驶人在左转和环形匝道下的心理负荷值明显大于男性,且左转弯对女性影响更大。愤怒型驾驶人的心理负荷高于焦虑型和冒险型,右转弯对愤怒型驾驶人影响更大。
(4)立交匝道曲线段行驶时,驾驶人的心理负荷随道路环境熟悉程度增加而减小,随转弯角度增加而增大。匝道车道数越多,车辆交织行为越复杂,心理负荷越大。右转匝道中,心理负荷与匝道半径呈负相关;左转和环形匝道中,心理负荷存在波动,连续的环形匝道行驶会增加心理负荷。
(5)提出匝道区域风险改善意见如下:在右转匝道设计中,应增大曲线半径以降低驾驶人心理负荷。左转匝道应设计平滑,减少急转弯和陡坡。环形匝道应设置清晰视觉引导标志,帮助驾驶人提前预判路线,减少心理负荷。立交匝道区域应设置清晰导航标志,增加环境熟悉度,降低心理负荷。
基金
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  • 国家自然科学基金(52172340)
  • 重庆市高校创新研究群体项目(CXQT21022)
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2025年第25卷第16期
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doi: 10.12404/j.issn.1671-1815.2405683
  • 接收时间：2024-07-29
  • 首发时间：2025-07-09
  • 出版时间：2025-06-08
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  • 收稿日期：2024-07-29
  • 修回日期：2025-03-14
基金
国家自然科学基金(52172340)
重庆市高校创新研究群体项目(CXQT21022)
作者信息
    1 重庆交通大学交通运输学院, 重庆 400074
    2 中交第二公路勘察设计研究院有限公司, 武汉 430058
    3 新疆农业大学交通与物流工程学院, 乌鲁木齐 830052

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* 徐进(1977—),男,汉族,吉林四平人,博士,教授,博士研究生导师。研究方向:道路安全性设计、车路协同与驾驶行为。E-mail:。
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