ETH Future Cities Simulation Platform

Halatsch, Jan; Kunze, Antje; Burkhard, Remo; Schmitt, Gerhard

doi:10.1007/978-3-540-69002-3_8

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Crowd simulations (generally, but not exclusively, using an agent-based approach) are now employed in many different domains, from events planning and management [ 4 ], to urban design [ 5 ], and incident response and analysis [ 6 , 7 ]. By studying flows of people en masse , and their interactions with the environment and with one another, researchers aim to better understand human collective social behaviour, design more effective and enjoyable public spaces, and improve levels of safety, security and well-being [ 8 ]. An important open question concerns the believability of crowd simulations in terms of their macroscopic properties.…”

Section: Introductionmentioning

confidence: 99%

A Turing test for crowds

Webster

Amos

2020

R. Soc. open sci.

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The accuracy and believability of crowd simulations underpins computational studies of human collective behaviour, with implications for urban design, policing, security and many other areas. Accuracy concerns the closeness of the fit between a simulation and observed data, and believability concerns the human perception of plausibility. In this paper, we address both issues via a so-called ‘Turing test’ for crowds, using movies generated from both accurate simulations and observations of real crowds. The fundamental question we ask is ‘Can human observers distinguish between real and simulated crowds?’ In two studies with student volunteers ( n = 384 and n = 156), we find that non-specialist individuals are able to reliably distinguish between real and simulated crowds when they are presented side-by-side, but they are unable to accurately classify them. Classification performance improves slightly when crowds are presented individually, but not enough to out-perform random guessing. We find that untrained individuals have an idealized view of human crowd behaviour which is inconsistent with observations of real crowds. Our results suggest a possible framework for establishing a minimal set of collective behaviours that should be integrated into the next generation of crowd simulation models.

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