{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dimensionless-numbers-reveal-distinct-regimes","title":"Dimensionless Numbers Reveal Distinct Regimes in the Structure and Dynamics of Pedestrian Crowds","arxiv_id":"2307.12786","date":"2023-07-24","proceeding":null,"authors":["Jakob Cordes","Andreas Schadschneider","Alexandre Nicolas"],"abstract":"In fluid mechanics, dimensionless numbers like the Reynolds number help classify flows. We argue that such a classification is also relevant for crowd flows by putting forward the dimensionless Intrusion and Avoidance numbers.Using an extensive dataset, we show that these delineate regimes that are characterized by distinct structural signatures, best probed in terms of distances at low Avoidance number and times-to-collision at low Intrusion number.These findings prompt a perturbative expansion of the agent-based dynamics; the generic models thus obtained perform well in (and only in) the regime in which they were derived.","url_abs":"https://arxiv.org/abs/2307.12786v2","url_pdf":"https://arxiv.org/pdf/2307.12786v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"dimensionless-numbers-reveal-distinct-regimes","repo_url":"https://github.com/jakob-kku/pedestrian_models","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}