Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions

By Dirk Helbing, Lubos Buzna, Anders Johansson, Torsten Werner. Transportation Science, 2005

1,516 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Ocean Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/f019f0ae597fb151856c80ad1964354b214abbf2

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Citations in the corpus

Evacuation Dynamics: Empirical Results, Modeling and Applications (2008, background): “In experiments [53] an increase of the flow of about 30% has been observed for a door with b = 0.”
The fundamental diagram of pedestrian movement revisited (2005, neutral): “ar X iv :p hy si cs /0 50 61 70 v2 [ ph ys ic s. so Keywords: Traffic and crowd dynamics”
The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics (2010, background): “, the physical constraints would prevail over the social preferences, and group members would start walking one behind another, forming a ‘river-like’ following pattern, as reported by Helbing et al. [28]. One may also ask how groups with more than four members would organise. It is, in fact, unlike”
Self-organised complex aerial displays of thousands of starlings: a model (2009, background): “So far biologically-oriented models of swarming have shown that stable swarming patterns may arise by self-organisation from rules for movement and interactions with others close by (Buhl et al., 2006; Couzin et al., 2005; Helbing et al., 2005; Hemelrijk, 2002; Parrish and Edelstein-Keshet, 1999).”
From Crowd Dynamics to Crowd Safety: a Video-Based Analysis (2008, methodology): “Therefore, we also generated pedestrian distributions resulting from a pedestrian simulation with the social force model [14], which took into account the finite space requirements (“diameters”) of pedestrians.”
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction (2020, background): “, crowd analysis [18] and robotics [13].”
Ordering in bidirectional pedestrian flows and its influence on the fundamental diagram (2011, background): “[23] concluded t hat counterflows are significantly more efficient than unidirectional flows.”
Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data (2007, background): “These spatiotemporal phenomena include the formation of lanes of uniform walking direction in crowds with oppositely moving pedestrians, stripe formation in two intersecting pedestrian streams, and oscillations of the passing direction at bottlenecks under normal conditions with moderate densities [”
SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction (2019, background): “Early work from Helbing and Molnar [14] models the interaction be-tween humans as “social force”, which is proved to be effective and applied to crowd analysis [13, 29] and robotics [8, 32].”