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Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety

Author

Listed:
  • Lei Han

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China)

  • Zhigang Du

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China)

  • Shoushuo Wang

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China)

  • Ying Chen

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China)

Abstract

To study the influence of traffic signs information volume (TSIV) on drivers’ visual characteristics and driving safety, the simulation scenarios of different levels of TSIV were established, and 30 participants were recruited for simulated driving tests. The eye tracker was used to collect eye movement data under three-speed conditions (60 km/h, 80 km/h, and 100 km/h) and different levels of TSIV (0 bits/km, 10 bits/km, 20 bits/km, 30 bits/km, 40 bits/km, and 50 bits/km). Principal component analysis (PCA) was used to select indicators sensitive to the influence of TSIV on the drivers’ visual behavior and to analyze the influence of TSIV on the drivers’ visual characteristics and visual workload intensity under different speed conditions. The results show that the fixation duration, saccade duration, and saccade amplitude are the three eye movement indicators that are most responsive to changes in the TSIV. The driver’s visual characteristics perform best at the S3 TSIV level (30 bits/km), with the lowest visual workload intensity, indicating that drivers have the lowest psychological stress and lower driving workload when driving under this TSIV condition. Therefore, a density of 30 bits/km is suggested for the TSIV, in order to ensure the security and comfort of the drivers. The theoretical underpinnings for placing and optimizing traffic signs will be provided by this work.

Suggested Citation

  • Lei Han & Zhigang Du & Shoushuo Wang & Ying Chen, 2022. "Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety," IJERPH, MDPI, vol. 19(16), pages 1-23, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:16:p:10349-:d:892800
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    References listed on IDEAS

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    1. Kun Liu & Hongxing Deng, 2021. "The Relationship of the Information Quantity of Urban Roadside Traffic Signs and Drivers’ Visibility Based on Information Transmission," IJERPH, MDPI, vol. 18(20), pages 1-14, October.
    2. Nengchao Lyu & Lian Xie & Chaozhong Wu & Qiang Fu & Chao Deng, 2017. "Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China," IJERPH, MDPI, vol. 14(2), pages 1-25, February.
    3. Yanli Ma & Shouming Qi & Yaping Zhang & Guan Lian & Weixin Lu & Ching-Yao Chan, 2020. "Drivers’ Visual Attention Characteristics under Different Cognitive Workloads: An On-Road Driving Behavior Study," IJERPH, MDPI, vol. 17(15), pages 1-19, July.
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    Cited by:

    1. Lian Xie & Jiaxin Zhang & Rui Cheng, 2023. "Comprehensive Evaluation of Freeway Driving Risks Based on Fuzzy Logic," Sustainability, MDPI, vol. 15(1), pages 1-20, January.
    2. Qin Zeng & Yun Chen & Xiazhong Zheng & Shiyu He & Donghui Li & Benwu Nie, 2023. "Optimization of Underground Cavern Sign Group Layout Using Eye-Tracking Technology," Sustainability, MDPI, vol. 15(16), pages 1-32, August.

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