Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Dataset from the Festival of Lights in Lyon
Abstract The dynamics of dense crowds have received considerable attention from researchers seeking fundamental understanding or aiming to develop data-driven algorithms to predict pedestrian trajectories. However, current research mainly relies on data collected in controlled settings. We present o...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04732-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849420505578209280 |
|---|---|
| author | Oscar Dufour Huu-Tu Dang Jakob Cordes Raphael Korbmacher Mohcine Chraibi Benoit Gaudou Alexandre Nicolas Antoine Tordeux |
| author_facet | Oscar Dufour Huu-Tu Dang Jakob Cordes Raphael Korbmacher Mohcine Chraibi Benoit Gaudou Alexandre Nicolas Antoine Tordeux |
| author_sort | Oscar Dufour |
| collection | DOAJ |
| description | Abstract The dynamics of dense crowds have received considerable attention from researchers seeking fundamental understanding or aiming to develop data-driven algorithms to predict pedestrian trajectories. However, current research mainly relies on data collected in controlled settings. We present one of the first comprehensive field datasets describing dense pedestrian dynamics at different scales, from contextualized macroscopic crowd flows over hundreds of meters to microscopic trajectories (around 7000 individual trajectories). In addition, a sample of GPS traces, some statistics of contacts and pushes, and a list of non-standard crowd phenomena observed in the video recordings are provided. The data were collected during the 2022 Festival of Lights in Lyon, France, as part of the French-German MADRAS project and cover densities up to 4 pedestrians per square meter. We suggest using this extensive dataset, acquired in complex real-world settings, to benchmark models of pedestrian dynamics. |
| format | Article |
| id | doaj-art-59418136d1684e9d9a29d7d2e6ba09aa |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-59418136d1684e9d9a29d7d2e6ba09aa2025-08-20T03:31:44ZengNature PortfolioScientific Data2052-44632025-04-0112111010.1038/s41597-025-04732-3Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Dataset from the Festival of Lights in LyonOscar Dufour0Huu-Tu Dang1Jakob Cordes2Raphael Korbmacher3Mohcine Chraibi4Benoit Gaudou5Alexandre Nicolas6Antoine Tordeux7Université Claude Bernard Lyon 1, CNRS, Institut Lumière MatièreUMR 5505 IRIT, Université Toulouse CapitoleInstitute of Advanced Simulation, Forschungszentrum Jülich GmbHFakultät für Maschinenbau und Sicherheitstechnik, Bergische Universität WuppertalInstitute of Advanced Simulation, Forschungszentrum Jülich GmbHUMR 5505 IRIT, Université Toulouse CapitoleUniversité Claude Bernard Lyon 1, CNRS, Institut Lumière MatièreFakultät für Maschinenbau und Sicherheitstechnik, Bergische Universität WuppertalAbstract The dynamics of dense crowds have received considerable attention from researchers seeking fundamental understanding or aiming to develop data-driven algorithms to predict pedestrian trajectories. However, current research mainly relies on data collected in controlled settings. We present one of the first comprehensive field datasets describing dense pedestrian dynamics at different scales, from contextualized macroscopic crowd flows over hundreds of meters to microscopic trajectories (around 7000 individual trajectories). In addition, a sample of GPS traces, some statistics of contacts and pushes, and a list of non-standard crowd phenomena observed in the video recordings are provided. The data were collected during the 2022 Festival of Lights in Lyon, France, as part of the French-German MADRAS project and cover densities up to 4 pedestrians per square meter. We suggest using this extensive dataset, acquired in complex real-world settings, to benchmark models of pedestrian dynamics.https://doi.org/10.1038/s41597-025-04732-3 |
| spellingShingle | Oscar Dufour Huu-Tu Dang Jakob Cordes Raphael Korbmacher Mohcine Chraibi Benoit Gaudou Alexandre Nicolas Antoine Tordeux Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Dataset from the Festival of Lights in Lyon Scientific Data |
| title | Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Dataset from the Festival of Lights in Lyon |
| title_full | Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Dataset from the Festival of Lights in Lyon |
| title_fullStr | Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Dataset from the Festival of Lights in Lyon |
| title_full_unstemmed | Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Dataset from the Festival of Lights in Lyon |
| title_short | Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Dataset from the Festival of Lights in Lyon |
| title_sort | dense crowd dynamics and pedestrian trajectories a multiscale field dataset from the festival of lights in lyon |
| url | https://doi.org/10.1038/s41597-025-04732-3 |
| work_keys_str_mv | AT oscardufour densecrowddynamicsandpedestriantrajectoriesamultiscalefielddatasetfromthefestivaloflightsinlyon AT huutudang densecrowddynamicsandpedestriantrajectoriesamultiscalefielddatasetfromthefestivaloflightsinlyon AT jakobcordes densecrowddynamicsandpedestriantrajectoriesamultiscalefielddatasetfromthefestivaloflightsinlyon AT raphaelkorbmacher densecrowddynamicsandpedestriantrajectoriesamultiscalefielddatasetfromthefestivaloflightsinlyon AT mohcinechraibi densecrowddynamicsandpedestriantrajectoriesamultiscalefielddatasetfromthefestivaloflightsinlyon AT benoitgaudou densecrowddynamicsandpedestriantrajectoriesamultiscalefielddatasetfromthefestivaloflightsinlyon AT alexandrenicolas densecrowddynamicsandpedestriantrajectoriesamultiscalefielddatasetfromthefestivaloflightsinlyon AT antoinetordeux densecrowddynamicsandpedestriantrajectoriesamultiscalefielddatasetfromthefestivaloflightsinlyon |