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作者:

Huang, Lihua (Huang, Lihua.) | Bian, Yang (Bian, Yang.) | Zhao, Xiaohua (Zhao, Xiaohua.) | Xu, Yachen (Xu, Yachen.) | Rong, Jian (Rong, Jian.) (学者:荣建)

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SSCI EI Scopus SCIE

摘要:

Complex diagrammatic guide signs (DGSs) are widely used. To study the influence of DGSs with different complexities on drivers' cognition, four types of typical DGSs on urban expressways in Beijing, China, were studied through a driving simulator experiment. Three indicators related to eye movement and three indicators related to driving behavior were collected and analyzed. The results indicated that the complexities of DGSs significantly increased the number of fixations, increased the number of saccades and decreased the duration of the first fixation on DGSs. More complex DGSs result in a greater difficulty for drivers in recognizing the destination on the DGSs. The complexities of DGSs also had a significant effect on driving behavior, including increasing the standard deviation of the average speed, increasing the driving distance during an acceleration change and increasing the average deceleration. When drivers observed a highly complex DGS, the vehicle speed appeared to significantly fluctuate and decrease, and drivers required a long time to comprehend the DGSs. Under the effect of different complex DGSs, indicators of the drivers' eye movement and driving behavior are closely correlated, and the average relational degree is 0.645. In particular, relations of the number of saccades on DGSs with three indicators of driving behavior and of the driving distance during an acceleration change with two indicators of eye movement are closer than the relations between other parameters. This study provides a foundation for the influence of DGSs on drivers' brain cognition and the optimization of complex DGSs, which will be investigated in future research.

关键词:

Diagrammatic guide signs Driving behavior Eye movement Gray relational analysis Relational degrees

作者机构:

  • [ 1 ] [Huang, Lihua]Beijing Collaborat Innovat Ctr Metropolitan Trans, Beijing 100124, Peoples R China
  • [ 2 ] [Bian, Yang]Beijing Collaborat Innovat Ctr Metropolitan Trans, Beijing 100124, Peoples R China
  • [ 3 ] [Zhao, Xiaohua]Beijing Collaborat Innovat Ctr Metropolitan Trans, Beijing 100124, Peoples R China
  • [ 4 ] [Rong, Jian]Beijing Collaborat Innovat Ctr Metropolitan Trans, Beijing 100124, Peoples R China
  • [ 5 ] [Huang, Lihua]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing 100124, Peoples R China
  • [ 6 ] [Bian, Yang]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing 100124, Peoples R China
  • [ 7 ] [Zhao, Xiaohua]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing 100124, Peoples R China
  • [ 8 ] [Rong, Jian]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing 100124, Peoples R China
  • [ 9 ] [Xu, Yachen]Huizhou City Traff Planning Survey & Design Inst, Huizhou 516001, Peoples R China
  • [ 10 ] [Zhao, Xiaohua]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China

通讯作者信息:

  • [Zhao, Xiaohua]Beijing Collaborat Innovat Ctr Metropolitan Trans, Beijing 100124, Peoples R China;;[Zhao, Xiaohua]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing 100124, Peoples R China;;[Zhao, Xiaohua]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China

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来源 :

COGNITION TECHNOLOGY & WORK

ISSN: 1435-5558

年份: 2020

期: 2

卷: 22

页码: 373-388

2 . 6 0 0

JCR@2022

ESI学科: PSYCHIATRY/PSYCHOLOGY;

ESI高被引阀值:25

JCR分区:3

被引次数:

WoS核心集被引频次: 9

SCOPUS被引频次: 9

ESI高被引论文在榜: 0 展开所有

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中文被引频次:

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