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Optimization of Underground Cavern Sign Group Layout Using Eye-Tracking Technology

Author

Listed:
  • Qin Zeng

    (Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang 443002, China
    College of Economics & Management, China Three Gorges University, Yichang 443002, China
    College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China)

  • Yun Chen

    (Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang 443002, China
    College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China)

  • Xiazhong Zheng

    (Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang 443002, China
    College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China)

  • Shiyu He

    (College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China)

  • Donghui Li

    (Building Decoration Supervision Station, Yichang Municipal Housing and Urban-Rural Development Bureau, Yichang 443000, China)

  • Benwu Nie

    (CHN ENERGY Jinshajiang Branch Co., Ltd., Kunming 650000, China)

Abstract

Efficient sign layouts play a crucial role in guiding driving in underground construction caverns and enhancing transportation safety. Previous studies have primarily focused on evaluating drivers’ gaze behavior in tunnels to optimize individual traffic sign layouts. However, the lack of a theoretical framework for visual perception of visual capture and information conveyed by sign groups hinders the measurement of drivers’ comprehensive visual perception and the layout optimization of sign groups. To address this gap, this study introduces a calculation method for sign group information volume and a visual cognition model, establishing a comprehensive evaluation approach for sign group visual cognition. Eye movement data, collected using eye-tracking technology, were utilized to evaluate the comprehensive visual perception and optimize the layout of sign groups. The findings indicate that a low information volume fails to enhance recognition ability and alleviate the psychological burden. Conversely, excessive information may result in overlooking signs positioned on the left and top. Furthermore, drivers are unable to improve cognitive efficiency and driving safety even with self-regulation when faced with an information volume exceeding 120 bits within a 100 m span. Overall, this study demonstrates the effectiveness of the proposed method in promoting the long-term safety effect of temporary signage layouts in underground construction areas.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12604-:d:1221136
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    References listed on IDEAS

    as
    1. Lianzhen Wang & Han Zhang & Lingyun Shi & Qingling He & Huizhi Xu, 2021. "Optimization Model of Regional Traffic Signs for Inducement at Road Works," Sustainability, MDPI, vol. 13(13), pages 1-14, June.
    2. 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.
    3. John Liechty & Rik Pieters & Michel Wedel, 2003. "Global and local covert visual attention: Evidence from a bayesian hidden markov model," Psychometrika, Springer;The Psychometric Society, vol. 68(4), pages 519-541, December.
    4. Amin Azimian & Carlos Alberto Catalina Ortega & Juan Maria Espinosa & Miguel Ángel Mariscal & Susana García-Herrero, 2021. "Analysis of Drivers’ Eye Movements on Roundabouts: A Driving Simulator Study," Sustainability, MDPI, vol. 13(13), pages 1-10, July.
    5. Wenhui Zhang & Jing Dai & Yulong Pei & Penghui Li & Ying Yan & Xinqiang Chen, 2016. "Drivers’ Visual Search Patterns during Overtaking Maneuvers on Freeway," IJERPH, MDPI, vol. 13(11), pages 1-15, November.
    6. 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.
    7. Ting Shang & Hao Lu & Peng Wu & Yi Wei, 2021. "Eye-Tracking Evaluation of Exit Advance Guide Signs in Highway Tunnels in Familiar and Unfamiliar Drivers," IJERPH, MDPI, vol. 18(13), pages 1-14, June.
    8. Yong Fang & Jiayi Zhou & Hua Hu & Yanxi Hao & Dianliang Xiao & Shaojie Li, 2022. "Combination Layout of Traffic Signs and Markings of Expressway Tunnel Entrance Sections: A Driving Simulator Study," Sustainability, MDPI, vol. 14(6), pages 1-13, March.
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