Pedestrian Crowd Management Experiments: A Data Guidance Paper
DOI:
https://doi.org/10.17815/CD.2023.141Abstract
Understanding pedestrian dynamics and the interaction of pedestrians with their environment is crucial to the safe and comfortable design of pedestrian facilities. Experiments offer the opportunity to explore the influence of individual factors. In the context of the project CroMa (Crowd Management in transport infrastructures), experiments were conducted with about 1000 participants to test various physical and social psychological hypotheses focusing on people's behaviour at railway stations and crowd management measures. The following experiments were performed: i) Train Platform Experiment, ii) Crowd Management Experiment, iii) Single-File Experiment, iv) Personal Space Experiment, v) Boarding and Alighting Experiment, vi) Bottleneck Experiment and vii) Tiny Box Experiment. This paper describes the basic planning and implementation steps, outlines all experiments with parameters, geometries, applied sensor technologies and pre- and post-processing steps. All data can be found in the pedestrian dynamics data archive.
References
CroMa Project: Website CroMa Project. URL https://www.croma-projekt.de/de
CrowdDNA Project: Website CrowdDNA Project. URL https://crowddna.eu/
Forschungszentrum Jülich: Pedestrian dynamics data archive. Pedestrian Dynamics Data Archive (2022). doi:10.34735/ped.da
Forschungszentrum Jülich, Bergische Universität Wuppertal, Ruhr-Universität Bochum: Experiment outline and participant statistics for experiments within the CroMa project. Pedestrian Dynamics Data Archive (2022). doi:10.34735/PED.2021.2
Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F., Medina-Carnicer, R.: Generation of fiducial marker dictionaries using Mixed Integer Linear Programming. Pattern Recognition 51, 481-491 (2016). doi:10.1016/j.patcog.2015.09.023
Robert Koch Institut: 7-day incidence Germany (2021). URL https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/COVID-19-Trends/COVID-19-Trends.html?__blob=publicationFile#/home
Forschungszentrum Jülich, Bergische Universität Wuppertal, Ruhr-Universität Bochum: Train Platform Experiments. Pedestrian Dynamics Data Archive (2022). URL http://ped.fz-juelich.de/da/2021train_platform
Düsseldorf, F.: Merkblatt der Feuerwehr Düsseldorf - Einschätzen von Personendichten bei Veranstaltungen im Freien (2020). URL https://www.duesseldorf.de/fileadmin/Amt37/feuerwehr/dokudb/37_233/200120_Merkblatt_Feuerwehr_Duesseldorf_Einschaetzen_von_Personendichten_bei_Veranstaltungen.pdf
Forschungszentrum Jülich, Bergische Universität Wuppertal, Ruhr-Universität Bochum: Crowd Management Experiment. Pedestrian Dynamics Data Archive (2022). URL http://ped.fz-juelich.de/da/2021crowd_management
Forschungszentrum Jülich, Bergische Universität Wuppertal, Ruhr-Universität Bochum: Oval Experiment: Single File Motion. Pedestrian Dynamics Data Archive (2022). URL http://ped.fz-juelich.de/da/2021oval
Forschungszentrum Jülich, Bergische Universität Wuppertal, Ruhr-Universität Bochum: Personal Space Experiment. Pedestrian Dynamics Data Archive (2022). URL http://ped.fz-juelich.de/da/2021personal_space
Forschungszentrum Jülich, Bergische Universität Wuppertal, Ruhr-Universität Bochum: Boarding and Alighting Experiment. Pedestrian Dynamics Data Archive (2022). URL http://ped.fz-juelich.de/da/2021boarding_alighting
Forschungszentrum Jülich, Bergische Universität Wuppertal, Ruhr-Universität Bochum: Tiny Box Experiment. Pedestrian Dynamics Data Archive (2022). URL https://ped.fz-juelich.de/da/doku.php?id=tiny_box
Forschungszentrum Jülich, Bergische Universität Wuppertal, Ruhr-Universität Bochum: Bottleneck Experiment. Pedestrian Dynamics Data Archive (2022). URL http://ped.fz-juelich.de/da/2021bottleneck
Tentacle Sync GmbH: Tentacle sync E - operating manual v1.0. URL https://tentaclesync.com/files/documents/Tentacle_Sync_E_Operating_Manual_ENG.pdf
Rosendahl Studiotechnik GmbH: Instruction Manual Rosendahl nanosyncs HD. URL https://rosendahl-studiotechnik.com/nanosyncs_hd_20.pdf
Boltes, M., Adrian, J., Boomers, A.K., Brualla, R.M., Graf, A., Häger, P., Hillebrand, D., Kilic, D., Lieberenz, P., Salden, D., Schrödter, T.: PeTrack CroMa (2022-03-01). doi:10.5281/zenodo.6320753
Boltes, M., Seyfried, A.: Collecting pedestrian trajectories. Neurocomputing, Special Issue on Behaviours in Video 100, 127-133 (2013). doi:10.1016/j.neucom.2012.01.036
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD'96, p. 226–231. AAAI Press (1996)
Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X.: DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS) 42(3), 1-21 (2017)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI'81, pp. 674-679. Morgan Kaufmann Publishers Inc. (1981)
Lucas, B.D.: Generalized image matching by the method of differences (1984)
Boltes, M., Seyfried, A., Steffen, B., Schadschneider, A.: Automatic extraction of pedestrian trajectories from video recordings. In: Klingsch, W.W.F., Rogsch, C., Schadschneider, A., Schreckenberg, M. (eds.) Pedestrian and Evacuation Dynamics 2008, pp. 43-54. Springer Berlin Heidelberg (2010). doi:10.1007/978-3-642-04504-2_3
Movisens EdaMove4 UserManual: User Manual EdaMove4. URL https://docs.movisens.com/Sensors/EdaMove4/#welcome
Movisens EcgMove4 UserManual: EcgMove 4 | movisens Docs. URL https://docs.movisens.com/Sensors/EcgMove4/
Tekscan PMS5400N: Datasheet: Tekscan Pressure Mapping Sensor 5400N. URL https://www.tekscan.com/sites/default/files/resources/IDL-Pressure-Mapping-Sensor-5400N-Datasheet_0.pdf
Xsensor LX210:50.50.05: Datasheet: Xsensor SENSORS LX210:50.50.05. URL https://www.xsensor.de/wp-content/uploads/2015/11/LX210_50_50_05-2500-Sensoren-508mm-Aufl%C3%B6sung-Gr%C3%B6sse-25x25cm-Messbereich-014-11Ncm2-Anwendung-Sitze_Rollst%C3%BChle.pdf
Xsensor LX210:25.50.05: Datasheet: Xsensor SENSORS LX210:25.50.05. URL https://www.xsensor.de/wp-content/uploads/2016/09/Katalog-Industrie.pdf
Schepers, M., Giuberti, M., Bellusci, G.: Xsens MVN: Consistent Tracking of Human Motion Using Inertial Sensing (2018). doi:10.13140/RG.2.2.22099.07205
Xsens: MVN User Manual, Revision Z, 20 04 2022. URL https://www.xsens.com/hubfs/MVN_User_Manual.pdf
Boltes, M., Adrian, J., Raytarowski, A.K.: A Hybrid Tracking System of Full-Body Motion Inside Crowds. Sensors 21(6), 2108 (2021-03-17). doi:10.3390/s21062108
HappyOrNot: Smiley terminal – happyornot – feedbacksystem. URL https://www.happy-or-not.com/de/losung/smiley-terminal/
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Ann Katrin Boomers, Maik Boltes, Juliane Adrian, Mira Beermann, Mohcine Chraibi, Sina Feldmann, Frank Fiedrich, Niklas Frings, Arne Graf, Alica Kandler, Deniz Kilic, Krisztina Konya, Mira Küpper, Andreas Lotter, Helena Lügering, Francesca Müller, Sarah Paetzke, Anna-Katharina Raytarowski, Olga Sablik, Tobias Schrödter, Armin Seyfried, Anna Sieben, Ezel Üsten
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to Collective Dynamics agree to publish their articles under the Creative Commons Attribution 4.0 license.
This license allows:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Authors retain copyright of their work. They are permitted and encouraged to post items submitted to Collective Dynamics on personal or institutional websites and repositories, prior to and after publication (while providing the bibliographic details of that publication).
