Estimating people flow and crowdedness for various urban environments based on BLE signal sensing: Practical studies

To realize Society 5.0, the construction of urban digital twins is an urgent task. Among the various concern, human mobility is one of the most critical aspects that has attracted significant attention, and numerous approaches have been proposed so far. For example, the methods using CCTV cameras or LiDAR could perform high quality estimation, but they require the acquisition of sensitive data, e.g., video footage or gait data, and pose challenges of social acceptability due to the perception of surveillance. Therefore, this study aims to develop methods to estimate the number of people (crowdedness level) stay in various public spaces and the movement between places in the city (people flow) by utilizing WiFi and/or BLE (Bluetooth Low Energy) signals emitted by personally owned smartphones. In particular, this paper introduces a method for estimating crowdedness levels and people flow based on BLE advertising packet data (including RSSI and random addresses), and the five practical studies in the real-world settings.