The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Continuum crowds
Abstract
We present a real-time crowd model based on continuum dynamics. In our model, a dynamic potential field simultaneously integrates global navigation with moving obstacles such as other people, efficiently solving for the motion of large crowds without the need for explicit collision avoidance. Simulations created with our system run at interactive rates, demonstrate smooth flow under a variety of conditions, and naturally exhibit emergent phenomena that have been observed in real crowds.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Flocks, herds and schools: A distributed behavioral model
Reynolds, C. W. (1987) · Proceedings of the 14th annual conference on Computer graphics and interactive techniques · doi:10.1145/37401.37406 · Cited by 7,714
Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions
Helbing, D., Buzna, L., Johansson, A., & Werner, T. (2005) · Transportation Science · doi:10.1287/trsc.1040.0108 · Cited by 1,471
A continuum theory for the flow of pedestrians
Hughes, R. L. (2002) · Transportation Research Part B: Methodological · doi:10.1016/s0191-2615(01)00015-7 · Cited by 1,100
2000 IEEE International Conference on Robotics and Automation
康史, 八. (2000) · システム/制御/情報 : システム制御情報学会誌 · doi:10.11509/isciesci.44.9_543 · Cited by 1,071
Kirchner, A., & Schadschneider, A. (2002) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(02)00857-9 · Cited by 892
Fedkiw, R. P., Stam, J., & Jensen, H. W. (2001) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/383259.383260 · Cited by 877
Kirchner, A., & Schadschneider, A. (n.d.) · RePEc: Research Papers in Economics · Cited by 777
Self-Organizing Pedestrian Movement
Helbing, D., Molnár, P., Farkas, I. J., & Bolay, K. (2001) · Environment and Planning B: Planning and Design · doi:10.1068/b2697 · Cited by 751
Hughes, R. L. (2003) · Annual Review of Fluid Mechanics · doi:10.1146/annurev.fluid.35.101101.161136 · Cited by 652
Lamarche, F., & Donikian, S. (2004) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2004.00782.x · Cited by 271
Cited by
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00240 · Cited by 2,466
Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes
Robicquet, A., Sadeghian, A., Alahi, A., & Savarese, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_33 · Cited by 927
A survey of real-time strategy game AI research and competition in StarCraft
Ontanon, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., & Preuss, M. (2013) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2013.2286295 · Cited by 423
Learning pedestrian dynamics from the real world
Scovanner, P., & Tappen, M. F. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459224 · Cited by 135
Metadata
- Authors
- Treuille, A., Cooper, S., & Popović, Z.
- Venue
- ACM Transactions on Graphics
- Published
- 2006-07
- Volume
- 25
- Issue
- 3
- Pages
- 1160-1168
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2293527364
- Cited by
- 874
- References
- 19
- FWCI
- 113.19
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Continuum crowds. https://app.cochat.ai/papers/10.1145/1141911.1142008
- The paper
- Treuille, A., Cooper, S., & Popović, Z. (2006). Continuum crowds. ACM Transactions on Graphics. https://doi.org/10.1145/1141911.1142008
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.