In March 2011, the Great East Japan Earthquake occurred, killing approximately 20,000 people. Previous research has shown that evacuation start time and evacuation behavior are related to the disaster survival rate: immediate evacuation increases the survival rate and evacuation-disruption caused by traffic congestion decreases it. Therefore, it can be assumed that guiding people to safe locations will increase the survival rate. The detection of the human mobility flow is a key to rescuing more people, because its analysis can help determine the appropriate evacuation routes toward which people should be guided. The objective of our research is to develop a system for detecting the human mobility flows in a disaster scenario. We analyzed the requirements of human mobility flow detection for disaster evacuation guidance. In this paper, we propose a crowd sensing system that uses Bluetooth for recognizing human mobility flows. By detecting Bluetooth devices carried by pedestrians, the congestion degree can be estimated. Further, the devices’ movements can be detected by observing the received signal strength indicator (RSSI) of Bluetooth Low Energy (LE) beacons carried by pedestrians. The results of experimental evaluations of these two methods verify their usefulness. Our methods can estimate the congestion degree, as well as the velocity of walking pedestrians.

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