Paper
16 May 2024 Evaluation of apparent effectiveness of safety sign group in underground cavern construction
Qin Zeng, Yanhua Chen, Donghui Li
Author Affiliations +
Proceedings Volume 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024); 131600C (2024) https://doi.org/10.1117/12.3030669
Event: 4th International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 2024, Beijin, China
Abstract
Effective sign layouts are essential for guiding driving in underground construction caverns and improving transportation safety. While previous studies concentrated on evaluating drivers' gaze behavior in tunnels, the absence of a theoretical framework for visual perception of sign groups impedes comprehensive perception measurement and layout optimization. This paper aims to bridge this gap, which study explores drivers' visual cognition by analyzing eye movement and EEG indicators in sign group recognition tasks. It establishes an intuitive evaluation index system to gauge drivers' cognitive response efficiency to directional signs with varying information levels. The research evaluates the comprehensive efficiency of drivers' cognitive response in scenarios with different sign group information, focusing on the visual communication effectiveness of underground cavern sign groups. Utilizing the BCC model in the DEA method, the study obtains comprehensive evaluation results, shedding light on drivers' cognitive response to different information levels of directional signs. It includes an analysis of experimental results, discusses DEA ineffectiveness, and offers suggestions based on underground cavern sign group design requirements.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Qin Zeng, Yanhua Chen, and Donghui Li "Evaluation of apparent effectiveness of safety sign group in underground cavern construction", Proc. SPIE 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 131600C (16 May 2024); https://doi.org/10.1117/12.3030669
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KEYWORDS
Visualization

Electroencephalography

Eye

Safety

Decision making

Cognitive modeling

Roads

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