Exploring Distributed Acoustic Sensing for Pedestrian Monitoring: Signal Characteristics and Identification

Distributed acoustic sensing (DAS) has shown potential for traffic monitoring: when applied to fiber optic cables installed near roadways, it can capture vibrations from roadway activities, including higher-frequency surface waves from the dynamic interaction between vehicles and the road surface, and low-frequency pseudo-static signals from ground deformation because of loading and unloading near the fiber. While prior studies have largely focused on vehicle monitoring using telecommunication cables offset from roadways, this study explores the use of DAS for pedestrian monitoring, which introduces new challenges because of lower amplitude and variability in movement patterns. The primary experimental site featured fiber optic cables embedded in the roadway, providing strong fiber–road coupling and high-sensitivity, high-resolution capture of pedestrian activity. Controlled experiments were conducted to simulate walking, jogging, running, and jumping. Signal characteristics were examined in relation to movement type, speed, the individual pedestrian, the DAS gauge length, and variations in data processing, such as decimation, filtering, initial roadway strain, and min-max scaling. Random forest (RF) and K-nearest neighbor (KNN) algorithms were used to identify pedestrian ID from the jump data, demonstrating the ability to capture subtle differences in movement. To complement these controlled experiments and address real-world variability, additional pedestrian signals were explored from a secondary site where fiber was buried along a water pipeline beneath an active road in a residential area. Despite increased noise and reduced coupling, pedestrian signals remained detectable, providing additional evidence for DAS sensitivity to pedestrian movements. These findings support the broader applicability of DAS for pedestrian-aware urban traffic monitoring.