Every Move You Make: Patterns of Crowd Movement

  • Philip Ball

Abstract

Walking from here to there doesn’t seem like the most complex choice we face in our lives. Don’t we, like light beams or the proverbial crow, just take the most direct route, proceeding at a pace that suits us? But exploring and navigating our environment on foot is rarely so simple. What if there are obstacles in our path? Robotics engineers have long realized that it is no mean feat to find a compromise between directness and smoothness of trajectory, with no abrupt changes of direction. What if the terrain varies – some paved, some grassy or muddy? Trickiest of all, what if the objects in our way are themselves moving, if they are other pedestrians headed somewhere else? How crowds move around open spaces is a sophisticated process of collective negotiation that depends vitally on how we interact with one another.

Keywords

Direct Route Crowd Density Space Syntax Pedestrian Motion Complex Choice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Further Reading

  1. D. Helbing, J. Keltsch & P. Molnár, ‘Modelling the evolution of human trail systems’, Nature 388, 47–50 (1997).ADSCrossRefGoogle Scholar
  2. D. Helbing & P. Molnár, ‘Social force model for pedestrian dynamics’, Phys. Rev. E 51, 4282–4286 (1995).ADSCrossRefGoogle Scholar
  3. D. Helbing, I. Farkas & T. Vicsek, ‘Simulating dynamical features of escape panic’, Nature 407, 487–490 (2000).ADSCrossRefGoogle Scholar
  4. M. Batty, ‘Predicting where we walk’, Nature 388, 19–20 (1997).ADSCrossRefGoogle Scholar
  5. D. Helbing, A. Johansson & H. Z. Al-Abideen, ‘Dynamics of crowd disasters: an empirical study’, Phys. Rev. E 75, 046109 (2007)ADSCrossRefGoogle Scholar
  6. M. Moussaïd, D. Helbing & G. Theraulaz, ‘How simple rules determine pedestrian behavior and crowd disasters’, Proc. Natl. Acad. Sci. USA 108, 6884–6888 (2011).ADSCrossRefGoogle Scholar
  7. D. Roggen, M. Wirz, G. Tröster & D. Helbing, ‘Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods’, Networks and Heterogeneous Media 6, 521–544 (2011).MathSciNetCrossRefGoogle Scholar
  8. M. Batty, Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models and Fractals. MIT Press, Cambridge, Ma., 2005.Google Scholar
  9. http://www.spacesyntax.com/Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Philip Ball
    • 1
  1. 1.LondonUK

Personalised recommendations