  In large crowd events, there is always a potential possibility that a
stampede accident will occur. The accident may cause injuries or even death.
Approaches for controlling crowd flows and predicting dangerous congestion
spots would be a boon to on-site authorities to manage the crowd and to prevent
fatal accidents. One of the most popular approaches is real-time crowd
simulation based on position data from personal Global Positioning System (GPS)
devices. However, the accuracy of spatial data varies for different GPS
devices, and it is also affected by an environment in which an event takes
place. In this paper, we would like to assess the effect of position errors on
stampede prediction. We propose an Automatic Real-time dEtection of Stampedes
(ARES) method to predict stampedes for large events. We implement three
different stampede assessment methods in Menge framework and incorporate
position errors. Our analysis suggests that the probability of simulated
stampede changes significantly with the increase of the magnitude of position
errors, which cannot be eliminated entirely with the help of classic
techniques, such as the Kalman filter. Thus, it is our position that novel
stampede assessment methods should be developed, focusing on the detection of
position noise and the elimination of its effect.
