Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks
Utkarsh Singh, Jean‐François Determe, François Horlin, Philippe De Doncker
Abstract
To ensure effective management and security in large-scale public events, it is imperative for the event organizers to be aware of potentially critical crowd densities. This article, therefore, presents a solution to the above problem in terms of WiFi-based crowd counting and long short-term memory (LSTM) neural network-based forecasting. Monitoring of an actual event organized in Brussels has been described, wherein crowd counts are obtained using WiFi sensors in a privacy-preserved manner. The time-stamped crowd counts are used to develop univariate time-series, which are in-turn utilized for forecasting. Five different LSTM models are utilized for crowd time-series forecasting and analyzed for their suitability. A random walk model is used as a reference for performance assessment. Among different LSTM models, Convolutional LSTM delivered the best performance. Overall results and analysis show that the developed system is suitable for crowd monitoring.