A Study of the Detection of Pedestrian Flow Using Bluetooth Low Energy

Analysis of human mobility data provide us many important insights. For example, a detailed analysis of human mobility in urban space can provide important insights into gathering events. This insight is useful when addressing urban planning and public safety issues, and serves as a powerful tool for solving traffic congestion, early detection of social unrest, and so on. The analysis of human mobility in an indoor space such as in a museum exhibition, can assist us in anticipating the behavior of visitors; and in the early recognition of potential problems, such as the buildup of foot traffic at specific points. However, there is no universally accepted method for easily sensing human mobility. To address this problem, we developed a novel method to detect pedestrian flow using Bluetooth Low Energy (BLE). Our approach is based on the assumption that a BLE beacon is always affixed to the pedestrian. Thus, the individual’s velocity can be readily determined by analyzing the Received Signal Strength Indicator (RSSI) of their BLE beacon. Apart from velocity, the direction of the pedestrian can also be determined by detecting their BLE beacon with multiple sensors. In this investigation, we evaluated the proposed method using both experimental real- world data and simulations. Participants with BLE beacons walked straight in a hall while the RSSI of their beacons was monitored from a moving sensor. This information was used to estimate the velocities of the beacons. We also simulated the RSSIs of the beacons and estimated their velocities under various conditions. Our results indicate that the proposed method can precisely detect the velocities of pedestrians.