Simulation-Based Forecasts of Crowd Flows at Major Events Using Real-Time Measurements

Abstract

The complexity and dynamic nature of large events arise the need for decision makers to assess the current situation and to derive multi-temporal forecasts in order to identify critical situations in a timely manner and to initiate appropriate countermeasures. In this work, we present a fast mesoscopic simulation model which incorporates measurements from counting and Bluetooth sensors, thus providing real-time forecasts of crowd flows at major events. With this approach already a sparse placement of sensors at strategic points on an event area is sufficient to achieve the necessary spatial and temporal resolution for a complete characterization of the current crowd flows. For model verification and validation, we investigated case studies from two music festivals in Austria in 2012 and 2013 where extensive measurements on human motion data were obtained to evaluate the deviations of the simulation results from the measured walking times.