Back to Search
Start Over
Assessing crowd management strategies for the 2010 Love Parade disaster using computer simulations and virtual reality
- Source :
- Journal of the Royal Society Interface, Journal of the Royal Society. Interface, 17 (167)
- Publication Year :
- 2020
-
Abstract
- Dense crowds in public spaces have often caused serious security issues at large events. In this paper, we study the 2010 Love Parade disaster, for which a large amount of data (e.g. research papers, professional reports and video footage) exist. We reproduce the Love Parade disaster in a three-dimensional computer simulation calibrated with data from the actual event and using the social force model for pedestrian behaviour. Moreover, we simulate several crowd management strategies and investigate their ability to prevent the disaster. We evaluate these strategies in virtual reality (VR) by measuring the response and arousal of participants while experiencing the simulated event from a festival attendee’s perspective. Overall, we find that opening an additional exit and removing the police cordons could have significantly reduced the number of casualties. We also find that this strategy affects the physiological responses of the participants in VR.<br />Journal of the Royal Society. Interface, 17 (167)<br />ISSN:1742-5689<br />ISSN:1742-5662
- Subjects :
- Computer science
Internet privacy
Biomedical Engineering
Biophysics
Poison control
Bioengineering
02 engineering and technology
Pedestrian
Virtual reality
Biochemistry
Biomaterials
Disasters
Crowds
physiological arousal
spatial cognition
0202 electrical engineering, electronic engineering, information engineering
Parade
Humans
0501 psychology and cognitive sciences
Computer Simulation
crowd disasters
050107 human factors
Crowd simulation
Crowd disasters
Spatial cognition
Physiological arousal
Life Sciences–Engineering interface
crowd simulation
Event (computing)
business.industry
05 social sciences
Virtual Reality
Love
Crowding
Social force model
020201 artificial intelligence & image processing
business
Biotechnology
Research Article
Subjects
Details
- ISSN :
- 17425662 and 17425689
- Volume :
- 17
- Issue :
- 167
- Database :
- OpenAIRE
- Journal :
- Journal of the Royal Society, Interface
- Accession number :
- edsair.doi.dedup.....9856c96f482fede588908ff2a69b4ca3