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Road tunnels are enclosed spaces that most occupants only experience while driving through them. In case of fire, however, occupants potentially need to evacuate on foot from a dangerous and unfamiliar environment. Clear and accurate guidance is important for an efficient and safe evacuation from tunnels. Common cues for evacuation guidance are a signage and audio messages that attract occupants to move on appropriate egress routes and avoid unsafe routes. This paper investigates how different types of visual and auditory signals influence occupants’ exit choices in a simulated tunnel evacuation. Common guidance cues were presented to participants in a mobile Head Mounted Display, and they were asked to choose between two possible exit doors in a simulated road tunnel. Two attracting cues (‘‘EXIT’’ signs, audio instructions), and two detracting cues (‘‘DO NOT ENTER’’ signs; traffic cones placed in front of an exit) were studied in three virtual reality (VR) experiments. In each experiment, the presence and direction of the cues were manipulated, and data from 20 participants were collected. Experiment 1 explored the effects of attracting cues, Experiment 2 detracting cues, and Experiment 3 the combination of attracting and detracting cues. Across all studies, participants tended to follow the guidance provided when there was only one cue. When several competing and even contradictory cues were present, participants were most likely to rely on audio instructions, followed by traffic cones and ‘‘DO NOT ENTER’’ signs, whereas ‘‘EXIT’’ signs were often disregarded. We conclude that participants tend to follow temporary cues that could carry current information, as opposed to permanently installed signage. Some corresponding suggestions are put forward on evacuation system design and strategic planning in a tunnel fire.
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This content will become publicly available on March 1, 2026
Modeling competing guidance on evacuation choices under time pressure using virtual reality and machine learning
Efficient emergency guidance in buildings is essential for the safe evacuation of occupants. However, occupants may be exposed to contradictory information from signage and other sources of information. This study presents a set of forced-choice VR experiments and a machine learning approach to investigate the effect of competing or conflicting guidance on exit choice in simulated scenarios. In the VR study, participants chose between two potential exits under time pressure in each trial. Attracting cues (“EXIT” signs, audio instructions) and repelling cues (“DO NOT ENTER” signs, traffic cones) were placed in front of the two exits, either individually or in combination. In total, 2,125 datapoints were recorded from 20 participants. To model exit choice, machine learning (random forest, RF) models were applied to predict and interpret the guidance on evacuation choices. The tuned-hyperparameters RF model proposed in this study showed above 75% accuracy to predict evacuation choices facing conflict cues and was superior to default RF and logistic regression models. Interestingly, repelling cues such as “DO NOT ENTER” signs had a stronger impact on exit choice than attracting cues like “EXIT” signs when people have to make choices. Overall, the study offers valuable data and insights into exit choices, revealing that negative cues are more influential than positive ones in emergencies. These findings can significantly inform the design and optimization of egress guidance systems. This bias towards negative information under pressure suggests that evacuation systems should prioritize clear and prominent negative cues to guide occupants effectively.
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- Award ID(s):
- 1849446
- PAR ID:
- 10652664
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Expert Systems with Applications
- Volume:
- 262
- Issue:
- C
- ISSN:
- 0957-4174
- Page Range / eLocation ID:
- 125582
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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