Understanding influences on pedestrian movement is important to accurately simulate crowd behaviour, yet little research has explored the psychological factors that influence interactions between large groups in counterflow scenarios. Research from social psychology has demonstrated that social identities can influence the micro-level pedestrian movement of a psychological crowd, yet this has not been extended to explore behaviour when two large psychological groups are co-present. This study investigates how the presence of large groups with different social identities can affect pedestrian behaviour when walking in counterflow. Participants (N = 54) were divided into two groups and primed to have identities as either ‘team A’ or ‘team B’. The trajectories of all participants were tracked to compare the movement of team A when walking alone to when walking in counterflow with team B, based on their i) speed of movement and distance walked, and ii) proximity between participants. In comparison to walking alone, the presence of another group influenced team A to collectively self-organise to reduce their speed and distance walked in order to walk closely together with ingroup members. We discuss the importance of incorporating social identities into pedestrian group dynamics for empirically validated simulations of counterflow scenarios.
{"title":"Placing Large Group Relations into Pedestrian Dynamics: Psychological Crowds in Counterflow","authors":"A. Templeton, J. Drury, A. Philippides","doi":"10.17815/cd.2019.23","DOIUrl":"https://doi.org/10.17815/cd.2019.23","url":null,"abstract":"Understanding influences on pedestrian movement is important to accurately simulate crowd behaviour, yet little research has explored the psychological factors that influence interactions between large groups in counterflow scenarios. Research from social psychology has demonstrated that social identities can influence the micro-level pedestrian movement of a psychological crowd, yet this has not been extended to explore behaviour when two large psychological groups are co-present. This study investigates how the presence of large groups with different social identities can affect pedestrian behaviour when walking in counterflow. Participants (N = 54) were divided into two groups and primed to have identities as either ‘team A’ or ‘team B’. The trajectories of all participants were tracked to compare the movement of team A when walking alone to when walking in counterflow with team B, based on their i) speed of movement and distance walked, and ii) proximity between participants. In comparison to walking alone, the presence of another group influenced team A to collectively self-organise to reduce their speed and distance walked in order to walk closely together with ingroup members. We discuss the importance of incorporating social identities into pedestrian group dynamics for empirically validated simulations of counterflow scenarios.","PeriodicalId":93276,"journal":{"name":"Collective dynamics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41671463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pedestrian dynamics is concerned with understanding the movement patterns that arise in places where more than one person walks. Relating theoretical models to data is a crucial goal of research in this field. Statistical model fitting and model selection are a suitable approach to this problem and here we review the concepts and literature related to this methodology in the context of pedestrian dynamics. The central tenet of statistical modelling is to describe the relationship between different variables by using probability distributions. Rather than providing a critique of existing methodology or a "how to" guide for such an established research technique, our review aims to highlight broad concepts, different uses, best practices, challenges and opportunities with a focussed view on theoretical models for pedestrian behaviour. This contribution is aimed at researchers in pedestrian dynamics who want to carefully analyse data, relate a theoretical model to data, or compare the relative quality of several theoretical models. The survey of the literature we present provides many methodological starting points and we suggest that the particular challenges to statistical modelling in pedestrian dynamics make this an inherently interesting field of research.
{"title":"Statistical Model Fitting and Model Selection in Pedestrian Dynamics Research","authors":"N. Bode, E. Ronchi","doi":"10.17815/CD.2019.20","DOIUrl":"https://doi.org/10.17815/CD.2019.20","url":null,"abstract":"Pedestrian dynamics is concerned with understanding the movement patterns that arise in places where more than one person walks. Relating theoretical models to data is a crucial goal of research in this field. Statistical model fitting and model selection are a suitable approach to this problem and here we review the concepts and literature related to this methodology in the context of pedestrian dynamics. The central tenet of statistical modelling is to describe the relationship between different variables by using probability distributions. Rather than providing a critique of existing methodology or a \"how to\" guide for such an established research technique, our review aims to highlight broad concepts, different uses, best practices, challenges and opportunities with a focussed view on theoretical models for pedestrian behaviour. This contribution is aimed at researchers in pedestrian dynamics who want to carefully analyse data, relate a theoretical model to data, or compare the relative quality of several theoretical models. The survey of the literature we present provides many methodological starting points and we suggest that the particular challenges to statistical modelling in pedestrian dynamics make this an inherently interesting field of research.","PeriodicalId":93276,"journal":{"name":"Collective dynamics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48435359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juliane Adrian, N. Bode, M. Amos, Mitra Baratchi, Mira Beermann, M. Boltes, Alessandro Corbetta, G. Dezecache, J. Drury, Zhijian Fu, Roland Geraerts, S. Gwynne, G. Hofinger, A. Hunt, Tinus Kanters, A. Kneidl, K. Kónya, Gerta Köster, M. Küpper, Georgios Michalareas, F. Neville, Evangelos Ntontis, S. Reicher, E. Ronchi, A. Schadschneider, A. Seyfried, A. Shipman, A. Sieben, M. Spearpoint, G. Sullivan, A. Templeton, F. Toschi, Zeynep Yücel, F. Zanlungo, I. Zuriguel, Natalie Van der Wal, Frank van Schadewijk, Cornelia von Krüchten, Nanda Wijermans
This article presents a glossary of terms that are frequently used in research on human crowds. This topic is inherently multidisciplinary as it includes work in and across computer science, engineering, mathematics, physics, psychology and social science, for example. We do not view the glossary presented here as a collection of finalised and formal definitions. Instead, we suggest it is a snapshot of current views and the starting point of an ongoing process that we hope will be useful in providing some guidance on the use of terminology to develop a mutual understanding across disciplines. The glossary was developed collaboratively during a multidisciplinary meeting. We deliberately allow several definitions of terms, to reflect the confluence of disciplines in the field. This also reflects the fact not all contributors necessarily agree with all definitions in this glossary.
{"title":"A Glossary for\u0000Research on Human Crowd Dynamics","authors":"Juliane Adrian, N. Bode, M. Amos, Mitra Baratchi, Mira Beermann, M. Boltes, Alessandro Corbetta, G. Dezecache, J. Drury, Zhijian Fu, Roland Geraerts, S. Gwynne, G. Hofinger, A. Hunt, Tinus Kanters, A. Kneidl, K. Kónya, Gerta Köster, M. Küpper, Georgios Michalareas, F. Neville, Evangelos Ntontis, S. Reicher, E. Ronchi, A. Schadschneider, A. Seyfried, A. Shipman, A. Sieben, M. Spearpoint, G. Sullivan, A. Templeton, F. Toschi, Zeynep Yücel, F. Zanlungo, I. Zuriguel, Natalie Van der\u0000Wal, Frank van Schadewijk, Cornelia von Krüchten, Nanda Wijermans","doi":"10.17815/CD.2019.19","DOIUrl":"https://doi.org/10.17815/CD.2019.19","url":null,"abstract":"This article presents a glossary of terms that are frequently used in research on human crowds. This topic is inherently multidisciplinary as it includes work in and across computer science, engineering, mathematics, physics, psychology and social science, for example. We do not view the glossary presented here as a collection of finalised and formal definitions. Instead, we suggest it is a snapshot of current views and the starting point of an ongoing process that we hope will be useful in providing some guidance on the use of terminology to develop a mutual understanding across disciplines. The glossary was developed collaboratively during a multidisciplinary meeting. We deliberately allow several definitions of terms, to reflect the confluence of disciplines in the field. This also reflects the fact not all contributors necessarily agree with all definitions in this glossary. ","PeriodicalId":93276,"journal":{"name":"Collective dynamics","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74349770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}