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Understanding step synchronization in social groups: A novel method to recognize group

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  • Liu, Weisong
  • Zhang, Jun
  • Rasa, Abdul Rahim
  • Li, Xudong
  • Ren, Xiangxia
  • Song, Weiguo

Abstract

This study investigates the impact of social groups on the movement characteristics of pedestrian crowds. Through field observations and laboratory experiments, we observed step synchronization in social groups. To identify these groups, we developed a synchronization-based group recognition algorithm that leverages the trajectories of pedestrian heads during movement for the first time. By considering the distance, speed, and moving direction, our algorithm can detect social groups in crowds more accurately. Further, experimental tests with dyads and triples were conducted to evaluate the effectiveness and robustness of the algorithm. It is shown that the algorithm is able to calculate the synchronization ratios of pedestrians belonging to same social groups with a high degree of accuracy, ranging from 64% to 100%, which is significantly higher than those in other pedestrian combinations which ranged from 12% to 44%. Our findings demonstrate that the algorithm is capable of accurately distinguishing social groups within crowds.

Suggested Citation

  • Liu, Weisong & Zhang, Jun & Rasa, Abdul Rahim & Li, Xudong & Ren, Xiangxia & Song, Weiguo, 2023. "Understanding step synchronization in social groups: A novel method to recognize group," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    • Handle: RePEc:eee:phsmap:v:628:y:2023:i:c:s0378437123007264
    DOI: 10.1016/j.physa.2023.129171
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    References listed on IDEAS

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    5. Zeng, Guang & Cao, Shuchao & Liu, Chi & Song, Weiguo, 2018. "Experimental and modeling study on relation of pedestrian step length and frequency under different headways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 237-248.
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