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Data for "Local interactions underlying collective motion in human crowds"

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Overview

Year:
2017
Contributor:
Rio, Kevin W. (creator)
Dachner, Gregory C. (creator)
Warren, William H. (creator)
Subject:
Collective behavior
Crowd dynamics
Pedestrian dynamics
Flocking
Agent-based models
Self-organization
Extent:
1 zip file
DOI
https://doi.org/10.7301/Z02V2D92

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Abstract:
It is commonly believed that global patterns of motion in flocks, schools, and crowds emerge from local interactions between individuals, through a process of self-organization. The key to explaining such collective behavior thus lies in characterizing the nature of these local interactions. We take an experiment-driven approach to modelling collective motion in human crowds. Previously, we found that a pedestrian aligns their velocity vector (speed and heading direction) with that of a neighbor, and we modelled these binary alignment dynamics. Here we investigate how a pedestrian is influenced by multiple neighbors in a crowd. A participant walked in a virtual crowd whose speed and heading were manipulated. We find that binary interactions are linearly combined, consistent with superposition, while coupling strength decreases with distance but not eccentricity. These results are supported by analysis of observational data on a human ‘swarm’. We model a pedestrian’s neighborhood as (a) circularly symmetric, (b) uni-directionally coupled, (c) a weighted average of neighbors within ±90˚ of the current heading, with (d) weights that decay exponentially to zero at 4m. The model enables us to simulate the experimental data and predict individual trajectories in the ‘swarm’. This approach yields the first bottom-up model of collective crowd motion.
Notes:
Funded by the National Institutes of Health, grant number R01EY010923

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Access Conditions

Use and Reproduction
This work is licensed under a Creative Commons Attribution 4.0 International License

Citation

Rio, Kevin W., Dachner, Gregory C., and Warren, William H., "Data for 'Local interactions underlying collective motion in human crowds'" (2017). Brown University Open Data Collection. Brown Digital Repository. Brown University Library. https://doi.org/10.7301/Z02V2D92

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    This collection contains open and publicly-funded data sets created by Brown University faculty and student researchers. Increasingly, publishers, and funders are requiring that protocols, data sets, metadata, and code underlying published research be retained and preserved, their locations cited within …
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