2026 Volume 77 Issue 2 Pages 85-93
A Low-cost technology capable of identifying the location of evacuees over a wide area is essential for navigating dense smoke conditions during fires in highway tunnels. This study proposes a novel human flow analysis method that addresses this challenge by utilizing Wi-Fi probe requests emitted by smartphones. In order to adapt to the increasing complexity of MAC address randomization for privacy protection, the present method employs six features that are independent of MAC addresses. It applies unsupervised learning, which does not require extensive pre-training, in order to estimate the number of mobile devices, which ensures real-time performance with low-cost equipment.
The present method's effectiveness has been validated through a field experiment simulating evacuation behavior in an actual tunnel. The results demonstrate that by deploying multiple Wi-Fi packet captures at regular intervals, it is possible to visualize time-series changes in the occupant numbers with a high accuracy. Such device count estimation achieved superior accuracy compared to previous RSSI threshold-based methods, particularly for small groups, and successfully visualized the movement of occupants between areas over time. However, the study also identified remaining challenges, including increased estimation errors for large groups, and a tendency to underestimate the actual number of people in real-world environments due to signal shielding by human bodies.
