Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment.
ENVIRON PLANN B
473 - 490.
Gibson's ecological theory of perception has received considerable attention within psychology literature, as well as in computer vision and robotics. However, few have applied Gibson's approach to agent-based models of human movement, because the ecological theory requires that individuals have a vision-based mental model of the world, and for large numbers of agents this becomes extremely expensive computationally. Thus, within current pedestrian models, path evaluation is based on calibration from observed data or on sophisticated but deterministic route-choice mechanisms; there is little open-ended behavioural modelling of human-movement patterns. One solution which allows individuals rapid concurrent access to the visual information within an environment is an 'exosomatic visual architecture" where the connections between mutually visible locations within a configuration are prestored in a lookup table. Here we demonstrate that, with the aid of an exosomatic visual architecture, it is possible to develop behavioural models in which movement rules originating from Gibson's principle of affordance are utilised. We apply large numbers of agents programmed with these rules to a built-environment example and show that, by varying parameters such as destination selection, field of view, and steps taken between decision points, it is possible to generate aggregate movement levels very similar to those found in an actual building context.
|Title:||Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment|
|Open access status:||An open access version is available from UCL Discovery|
|Keywords:||VISIBILITY GRAPHS, TRAFFIC FLOW, NAVIGATION, ANIMATION, ISOVISTS, CHOICE, SPACE, MODEL|
|UCL classification:||UCL > School of BEAMS > Faculty of the Built Environment > Built Environment Faculty Office|
Archive Staff Only