The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics
Open accessAbstract
Human crowd motion is mainly driven by self-organized processes based on local interactions among pedestrians. While most studies of crowd behaviour consider only interactions among isolated individuals, it turns out that up to 70% of people in a crowd are actually moving in groups, such as friends, couples, or families walking together. These groups constitute medium-scale aggregated structures and their impact on crowd dynamics is still largely unknown. In this work, we analyze the motion of approximately 1500 pedestrian groups under natural condition, and show that social interactions among group members generate typical group walking patterns that influence crowd dynamics. At low density, group members tend to walk side by side, forming a line perpendicular to the walking direction. As the density increases, however, the linear walking formation is bent forward, turning it into a V-like pattern. These spatial patterns can be well described by a model based on social communication between group members. We show that the V-like walking pattern facilitates social interactions within the group, but reduces the flow because of its "non-aerodynamic" shape. Therefore, when crowd density increases, the group organization results from a trade-off between walking faster and facilitating social exchange. These insights demonstrate that crowd dynamics is not only determined by physical constraints induced by other pedestrians and the environment, but also significantly by communicative, social interactions among individuals.
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Cites
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Self-citationSome functions of gaze-direction in social interaction
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Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions
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Self-citationDynamics of crowd disasters: An empirical study
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Self-citationThe principles of collective animal behaviour
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Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
Francis, A., Pérez-D’Arpino, C., Li, C., Xia, F., Alahi, A., Alami, R., Bera, A., Biswas, A., Biswas, J., Chandra, R., et al. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3700599 · Cited by 107
Complex Dynamical Systems in Social and Personality Psychology: Theory, Modeling, and Analysis
Richardson, M. J., Dale, R., & Marsh, K. L. (2014) · Handbook of Research Methods in Social and Personality Psychology · doi:10.1017/cbo9780511996481.015 · Cited by 104
Unblinking eyes: The ethics of automating surveillance
Macnish, K. (2012) · Ethics and Information Technology · doi:10.1007/s10676-012-9291-0 · Cited by 69
Evaluation of harvesting energy from pedestrians using piezoelectric floor tile energy harvester
Yingyong, P., Thainiramit, P., Jayasvasti, S., Thanach-Issarasak, N., & Isarakorn, D. (2021) · Sensors and Actuators A: Physical · doi:10.1016/j.sna.2021.113035 · Cited by 43
Metadata
- Authors
- Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., & Theraulaz, G.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-04-07
- Volume
- 5
- Issue
- 4
- Pages
- e10047
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2089584121
- Cited by
- 1,120
- References
- 26
- FWCI
- 104.64
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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