Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best.
Bron: Duives, D. C., van Oijen, T., & Hoogendoorn, S. P. (2020). Enhancing crowd monitoring system functionality through data fusion: Estimating flow rate from wi-fi traces and automated counting system data. Sensors (Switzerland), 20(21), 1-25. . https://doi.org/10.3390/s20216032