Blog
About

9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Identification of social relation within pedestrian dyads

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This study focuses on social pedestrian groups in public spaces and makes an effort to identify the type of social relation between the group members. As a first step for this identification problem, we focus on dyads (i.e. 2 people groups). Moreover, as a mutually exclusive categorization of social relations, we consider the domain-based approach of Bugental, which precisely corresponds to social relations of colleagues, couples, friends and families, and identify each dyad with one of those relations. For this purpose, we use anonymized trajectory data and derive a set of observables thereof, namely, inter-personal distance, group velocity, velocity difference and height difference. Subsequently, we use the probability density functions (pdf) of these observables as a tool to understand the nature of the relation between pedestrians. To that end, we propose different ways of using the pdfs. Namely, we introduce a probabilistic Bayesian approach and contrast it to a functional metric one and evaluate the performance of both methods with appropriate assessment measures. This study stands out as the first attempt to automatically recognize social relation between pedestrian groups. Additionally, in doing that it uses completely anonymous data and proves that social relation is still possible to recognize with a good accuracy without invading privacy. In particular, our findings indicate that significant recognition rates can be attained for certain categories and with certain methods. Specifically, we show that a very good recognition rate is achieved in distinguishing colleagues from leisure-oriented dyads (families, couples and friends), whereas the distinction between the leisure-oriented dyads results to be inherently harder, but still possible at reasonable rates, in particular if families are restricted to parent-child groups. In general, we establish that the Bayesian method outperforms the functional metric one due, probably, to the difficulty of the latter to learn observable pdfs from individual trajectories.

          Related collections

          Most cited references 25

          • Record: found
          • Abstract: not found
          • Article: not found

          Interpersonal attraction in exchange and communal relationships.

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics

            Human crowd motion is mainly driven by self-organized processes based on local interactions among pedestrians. While most studies of crowd behaviour consider only interactions among isolated individuals, it turns out that up to 70% of people in a crowd are actually moving in groups, such as friends, couples, or families walking together. These groups constitute medium-scale aggregated structures and their impact on crowd dynamics is still largely unknown. In this work, we analyze the motion of approximately 1500 pedestrian groups under natural condition, and show that social interactions among group members generate typical group walking patterns that influence crowd dynamics. At low density, group members tend to walk side by side, forming a line perpendicular to the walking direction. As the density increases, however, the linear walking formation is bent forward, turning it into a V-like pattern. These spatial patterns can be well described by a model based on social communication between group members. We show that the V-like walking pattern facilitates social interactions within the group, but reduces the flow because of its “non-aerodynamic” shape. Therefore, when crowd density increases, the group organization results from a trade-off between walking faster and facilitating social exchange. These insights demonstrate that crowd dynamics is not only determined by physical constraints induced by other pedestrians and the environment, but also significantly by communicative, social interactions among individuals.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Acquisition of the algorithms of social life: a domain-based approach.

              Proposing that the algorithms of social life are acquired as a domain-based process, the author offers distinctions between social domains preparing the individual for proximity-maintenance within a protective relationship (attachment domain), use and recognition of social dominance (hierarchical power domain), identification and maintenance of the lines dividing "us" and "them" (coalitional group domain), negotiation of matched benefits with functional equals (reciprocity domain), and selection and protection of access to sexual partners (mating domain). Flexibility in the implementation of domains occurs at 3 different levels: versatility at a bioecological level, variations in the cognitive representation of individual experience, and cultural and individual variations in the explicit management of social life. Empirical evidence for domain specificity was strongest for the attachment domain; supportive evidence was also found for the distinctiveness of the 4 other domains. Implications are considered at theoretical and applied levels.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Writing – review & editing
                Role: Software
                Role: Funding acquisitionRole: Project administrationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2019
                17 October 2019
                : 14
                : 10
                Affiliations
                [1 ] Department of Computer Science, Okayama University, Okayama, Japan
                [2 ] Intelligent Robotics and Communication Laboratory, ATR, Kyoto, Japan
                [3 ] Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
                [4 ] Department of Social Informatics, Kyoto University, Kyoto, Japan
                Universita degli Studi di Pisa, ITALY
                Author notes

                Competing Interests: The authors declare that no competing interests exist. Authors ZY, FZ and TK are affiliated to a private company, Advanced Telecommunications Research Institute International (ATR), respectively as visiting researcher (ZY), part-time contract researcher (FZ) and visiting group leader (TK). Nevertheless, their activity in ATR concerning this research work is related to the development of the government and university funded projects stated in the financial disclosure, and not to the development of commercial products or other economical interest of ATR. The authors state that no patent application is going to be pursued based on the research presented on this work, and that the results of this work are not going to be used to promote any kind of economical interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                [¤]

                Current address: OceaSciences Association, 30 Domaine de Goiffieu, Montagny, 69700, France

                Article
                PONE-D-19-05014
                10.1371/journal.pone.0223656
                6797107
                31622383
                © 2019 Yucel et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 5, Tables: 19, Pages: 27
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: J18K18168
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: 25287026
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100002241, Japan Science and Technology Agency;
                Award ID: JPMJCR17A2
                Award Recipient :
                ZY is supported by JSPS KAKENHI Grant Number J18K18168, Funder: Japan Society for the Promotion of Science, https://www.jsps.go.jp/english/. ZY is supported by WTT Startup Fund (No grant number available), Funder: Okayama University, http://www.okayama-u.ac.jp/index_e.html. ZY is affiliated to Advanced Telecommunications Research Institute International as visiting researcher to facilitate her collaboration with former colleagues, but does not receive a salary from this company. FZ is affiliated to Advanced Telecommunications Research Institute International. FZ is supported by JST CREST Program Grant Number JPMJCR17A2, Funder: Japan Society for the Promotion of Science, https://www.jsps.go.jp/english/. CF is affiliated to The University of Tokyo. CF is supported by JSPS KAKENHI Grant Number 25287026, Funder: Japan Society for the Promotion of Science, https://www.jsps.go.jp/english/. CF is supported by the Doctoral Student Special Incentives Program (SEUT RA) and the Foundation for Supporting International Students of the University of Tokyo, Funder: The University of Tokyo, https://www.u-tokyo.ac.jp/en/index.html. AG was affiliated to Grenoble-INP Ensimag and Okayama University during the term of this research. AG was supported by WTT Startup Fund during the term of this research (No grant number available), Funder: Okayama University, http://www.okayama-u.ac.jp/index_e.html. AG is currently a volunteer at OceaSciences association. TK is affiliated to Kyoto University. TK is affiliated to Advanced Telecommunications Research Institute International. TK is supported by JST CREST Program Grant Number JPMJCR17A2, Funder: Japan Society for the Promotion of Science, https://www.jsps.go.jp/english/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Bayesian Method
                Social Sciences
                Sociology
                Human Families
                Biology and Life Sciences
                Psychology
                Collective Human Behavior
                Social Sciences
                Psychology
                Collective Human Behavior
                Physical Sciences
                Physics
                Classical Mechanics
                Motion
                Velocity
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Density
                Biology and Life Sciences
                Psychology
                Behavior
                Social Sciences
                Psychology
                Behavior
                Biology and Life Sciences
                Psychology
                Social Psychology
                Social Sciences
                Psychology
                Social Psychology
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Sciences
                Sociology
                Social Networks
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized

                Comments

                Comment on this article