Comparison of three algorithms for real-time pedestrian state estimation-supporting a monitoring dashboard for large-scale events

Technical advancements allow for the development of crowd monitoring and management support systems to ensure the safety of pedestrians during large-scale events. This way, pedestrian behaviour can be monitored during events, and potential dangerous situations can be identified in a timely manner. A real-time prototype Crowd Monitoring Dashboard has been developed for a large nautical event in Amsterdam in 2015. Three main functions of such a system have been identified: the real-time data collection, the traffic engineering functions and the visualization. This paper focuses on three state estimation algorithms in the second component - the traffic engineering functions. These algorithms are presented and cross compared in terms of performance, data usage and practical applications, based on the empirical data collected from this large-scale event. The outcome can shed useful light on the feasibility of the data sources and estimation methods for future real-time applications on crowd monitoring and management for large-scale events.