The Impact of Position Errors on Crowd Simulation

TL;DR

This paper examines how position errors in GPS data affect crowd stampede prediction and introduces a new method for real-time detection that accounts for these inaccuracies.

Contribution

It presents an analysis of position error impacts on stampede prediction and proposes a novel real-time detection method that considers noise in GPS data.

Findings

Position errors significantly influence stampede probability predictions.

Classic noise reduction techniques like Kalman filters are insufficient to eliminate error effects.

New assessment methods should focus on detecting and mitigating position noise.

Abstract

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.