[1]郭凤香,马传戬,蔡晶,等.不同冲突情境下老年驾驶人焦虑水平预测分析*[J].中国安全生产科学技术,2024,20(9):219-226.[doi:10.11731/j.issn.1673-193x.2024.09.027]
GUO Fengxiang,MA Chuanjian,CAI Jing,et al.Predictive analysis on anxiety level of elderly drivers in different conflict situations[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(9):219-226.[doi:10.11731/j.issn.1673-193x.2024.09.027]
点击复制
不同冲突情境下老年驾驶人焦虑水平预测分析*
GUO Fengxiang,MA Chuanjian,CAI Jing,et al.Predictive analysis on anxiety level of elderly drivers in different conflict situations[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(9):219-226.[doi:10.11731/j.issn.1673-193x.2024.09.027]
《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]
- 卷:
- 20
- 期数:
- 2024年9期
- 页码:
- 219-226
- 栏目:
- 职业安全卫生管理与技术
- 出版日期:
- 2024-09-30
文章信息/Info
- Title:
- Predictive analysis on anxiety level of elderly drivers in different conflict situations
- 文章编号:
- 1673-193X(2024)-09-0219-08
- 分类号:
- X951；U492.8
- 文献标志码:
- A
- 摘要:
- 为研究冲突情景下老年驾驶人的焦虑水平，利用量表量化老年驾驶人焦虑程度，通过搭建冲突交叉口道路虚拟场景，采集不同冲突情境下老年驾驶人的驾驶行为数据。运用Spearman相关性分析法，筛选出与老年驾驶人焦虑水平相关的影响因子，基于径向基函数（RBF）神经网络和BP神经网络分别建立老年驾驶人焦虑水平预测模型，并对比2种模型的预测性能。研究结果表明：不同冲突情境下，老年驾驶人的年龄、驾龄、制动踏板深度、方向盘转角熵、冲突严重度等级与焦虑水平成显著正相关关系，速度与焦虑水平呈显著负相关关系；基于RBF神经网络的老年驾驶人焦虑模型的预测准确率为87.14%，精确率为88.24%，召回率为68.18%，F1值为76.92%。基于BP神经网络的老年驾驶人焦虑模型的预测准确率为92.86%，精确率为90.48%，召回率为83.36%，F1值为88.37%。2种模型均能够较好地预测老年驾驶人的焦虑水平，且基于BP神经网络的老年驾驶人焦虑预测模型预测性能更优。研究结果可为正确识别老年驾驶人的焦虑水平提供一定的理论基础，对于创造安全高效的驾驶具有重要意义。
- Abstract:
- To investigate the anxiety levels of elderly drivers in the conflict scenarios,a scale was employed to quantify the anxiety degree of elderly drivers.A virtual scenario of conflict intersection road was constructed to collect the driving behavior data of elderly drivers in different various conflict situations.The Spearman correlation analysis method was utilized to screen out the factors influencing the anxiety levels of elderly drivers.The prediction models for the anxiety levels of elderly drivers were established by using radial basis function (RBF) neural network and backpropagation (BP) neural network respectively,and the prediction performance of the two models was compared.The results show that in different conflict situations,the age,driving years,brake pedal depth,steering wheel angle entropy,and conflict severity present the significant positive correlation with the anxiety level,while the speed presents a significant negative correlation with the anxiety level.The prediction accuracy of the elderly driver anxiety model based on RBF neural network is 87.14%,the accuracy rate is 88.24%,the recall rate is 68.18%,and the F1 value is 76.92%.The prediction accuracy of the elderly driver anxiety model based on BP neural network is 92.86%,the accuracy rate is 90.48%,the recall rate is 83.36%,and the F1 value is 88.37%.Both models can better predict the anxiety level of elderly drivers,and the anxiety prediction model of elderly drivers based on BP neural network has better prediction performance.The research results can provide a theoretical basis for correctly identifying the anxiety level of elderly drivers,and are of great significance for creating the safe and efficient driving.
参考文献/References:
［1］周楚,李婵,沈先亭,等.老年驾驶人的驾驶自我调节研究述评 ［J］.中国公路学报,2020,33(6):18-28.
ZHOU Chu,LI Chan,SHEN Xianting,et al.Review of driving self-regulation in older adults ［J］.China Journal of Highway and Transport,2020,33(6):18-28.
［2］郭凤香,熊昌安,万华森,等.风险情境下老年驾驶人行为特性研究 ［J］.中国公路学报,2021,34(9):309-321.
GUO Fengxiang,XIONG Chang’an,WAN Huasen,et al.Behavioral characteristics of older drivers based on risk perception［J］.China Journal of Highway and Transport,2021,34(9):309-321.
［3］EHRING T,EHLERS A,GLUCKSMAN E.Do cognitive models help in predicting the severity of posttraumatic stress disorder,phobia,and depression after motor vehicle accidents?A prospective longitudinal study［J］.Journal of Consulting and Clinical Psychology,2008,76(2):219.
［4］王艳波,秦虹云,尤志栋,等.驾驶人心理因素对其风险驾驶行为的影响［J］.汽车与安全,2019(3):83-88.
WANG Yanbo,QIN Hongyun,YOU Zhidong,et al.Influence of driver’s psychological factors on their risky driving behavior ［J］.Auto & Safety,2019(3):83-88.
［5］孙舒放.1282名驾驶员焦虑水平与危险驾驶关系研究［J］.广东医学,2009,30(10):1413-1415.
［6］乔伟栋.驾驶人焦虑状态下的应激操作行为研究［D］.北京:北京建筑大学,2017.
［7］周子木.COVID-19疫情防控期间的驾驶员驾驶行为及焦虑心理分析［D］.石家庄：石家庄铁道大学,2023.
［8］杨佩佩,熊坚,何扬帆.公路小半径曲线路段驾驶人焦虑水平模型 ［J］.中国安全科学学报,2023,33(1):227-232.
YANG Peipei,XIONG Jian,He Yangfan.Anxiety level model of drivers on sharp circular curve road［J］.China Safety Science Journal,2023,33(1):227-232.
［9］BERGGREN N,DERAKSHAN N.Attentional control deficits in trait anxiety:why you see them and why you don’t［J］.Biological Psychology,2013,92(3):440-446.
［10］SHAMOA-NIR L.Road rage and aggressive driving behaviors:the role of state-trait anxiety and coping strategies［J］.Transportation Research Interdisciplinary Perspectives,2023,18:100780.
［11］YANG L,FENG Z,ZHAO X,et al.Analysis of the factors affecting drivers’ queue-jumping behaviors in China［J］.Transportation Research Part F:Traffic Psychology and Behaviour,2020,72:96-109.
［12］TAYLOR J E,CONNOLLY M J,BROOKLAND R,et al.Understanding driving anxiety in older adults［J］.Maturitas,2018,118:51-55
［13］HABIBIFAR N,SALMANZADEH H.Relationship between driving styles and biological behavior of drivers in negative emotional state［J］.Transportation Research Part F:Traffic Psychology and Behaviour,2022,85:245-258.
［14］夏映雪,曹凯,杜滕州.驾驶员自身因素与特质焦虑水平的关系研究［J］.山东理工大学学报:自然科学版,2013,27(1):9-12.
XIA Yingxue,CAO Kai,DU Tengzhou.The research of relations between the driver’s own factors and trait anxiety level ［J］.Journal of Shandong University of Technology:Natural Science Edition,2013,27(1):9-12.
［15］李娜.焦虑状态下驾驶员视觉搜索的眼动研究［D］.大连:辽宁师范大学,2014.
［16］陈亮,殷秀芬,胡立伟,等.基于模糊神经网络的驾驶模拟器实验有效性评价研究［J］.昆明理工大学学报(自然科学版),2021,46(2):126-134.
CHEN Liang,YIN Xiufen,HU Liwei,et al.Research on evaluation model of driving simulator experiment based on fuzzy neural network ［J］.Journal of Kunming University of Science and Technology (Natural Science),2021,46(2):126-134.
［17］SUN L,CHANG R S.Reliability and validity of the multidimensional driving style inventory in Chinese drivers［J］.Traffic Injury Prevention,2019,20(2):152-157.
［18］孙龙,杨程程,常若松.多维度驾驶风格量表的修订及初步应用［J］.人类工效学,2014,20(2):6-9.
SUN Long,YANG Chengcheng,CHANG Ruosong.revision and preliminary application of multidimensional driving style inventory ［J］.Chinese Journal of Ergonomics,2014,20(2):6-9.
［19］刘淼淼,鲁光泉,王云鹏,等.交叉口交通冲突严重程度量化方法［J］.交通运输工程学报,2012,12(3):120-126.
LIU Miaomiao,LU Guangquan,WANG Yunpeng,et al.Quantitative method of traffic severity at intersection ［J］.Journal of Traffic and Transportation Engineering,2012,12(3):120-126.
［20］李作进,李仁杰,李升波,等.基于方向盘转角近似熵与复杂度的驾驶人疲劳状态识别 ［J］.汽车安全与节能学报,2016,7(3):279-284.
LI Zuojin,LI Renjie,LI Shengbo,et al.Driver fatigue state recognition based on approximated entropy and complexity of steering wheel angel ［J］.Journal of Automotive Safety and Energy,2016,7(3):279-284.
［21］王雪松,游世凯,陈铭.基于驾驶模拟器的驾驶人制动后制动踏板操作行为分析［J］.交通信息与安全,2014,32(5):30-35.
WANG Xuesong,YOU Shikai,CHEN Ming.A driving simulator for collision a voidance behaviorafter Braking ［J］.Journal of Transport Information and Safety,2014,32(5):30-35.
［22］BOTA P J,WANG C,FRED A L N,et al.A review,current challenges,and future possibilities on emotion recognition using machine learning and physiological signals［J］.IEEE Access,2019,7:140990-141020.
［23］张俭让,王智鹏.基于多元线性回归的煤矿驾驶员情绪预测模型 ［J］.中国安全生产科学技术,2020,16(11):147-152.
ZHANG Jianrang,WANG Zhipeng.Prediction model for emotion of coal-mine driers based on multiple linear regression ［J］.Journal of Safety Science and Technology,2020,16(11):147-152.
［24］程静,张艺.不同情绪作用下的汽车驾驶行为预测 ［J］.交通运输工程与信息学报,2019,17(3):125-132.
CHENG Jing,ZHANG Yi.Prediction of driving behavior under different emotions ［J］.Journal of Transportation Engineering and Information,2019,17(3):125-132.
［25］中华人民共和国公安部.机动车运行安全技术条件：GB 7258—2017 ［S］.北京：中国标准出版社，2017.
相似文献/References:
备注/Memo
- 备注/Memo:
-
收稿日期： 2024-04-19
* 基金项目： 国家自然科学基金项目(71961012)；云南省教育厅科学研究基金项目（2024Y132）；昆明理工大学分析测试基金项目（2022M20202106029)
作者简介： 郭凤香，博士，教授，主要研究方向为车联网环境下驾驶行为分析、交通大数据挖掘与分析。
通信作者： 蔡晶，博士，副教授，主要研究方向为驾驶行为决策与规划、交通心理学与行为安全。
