Early-warning analysis of crowd stampede in metro station commercial area based on internet of things

Abstract

Crowd stampede has attracted significant attention of emergency management researchers in recent years. Early-warning of crowd stampede in metro station commercial area is discussed in this paper under the context of Internet of Things (IoT). Metro station commercial area is one of the entity carriers of E-commerce. IOT is a new concept of realizing intelligent sense, monitoring, tracking and management, which can be used in early-warning analysis of crowd stampede in metro station. Stampede risk early-warning in commercial area plays an important role in ensuring the operation of e-commerce online. Firstly, the laws and characteristics of the crowd movement in the commercial area of metro station are studied, which include the laeuna effect, block effect and aggravation effect. Secondly, the early-warning paradigm is constructed from four dimensions, ie. function, modules, principle and process. And then, under the IOT environment, the AHPsort II is applied to integrate the early-warning information and classify the stampede risk level. Finally, the paper takes the commercial area of Wuhan A metro station as an example to verify the practicability and effectiveness of the AHPsort II application to early-warning of crowd stampede in metro station commercial area.

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Acknowledgments

This research is supported by National Social Science Foundation of China (Project no. 15AGL021).

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Correspondence to Yanlan Mei.

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Xie, K., Mei, Y., Gui, P. et al. Early-warning analysis of crowd stampede in metro station commercial area based on internet of things. Multimed Tools Appl 78, 30141–30157 (2019). https://doi.org/10.1007/s11042-018-6982-5

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Keywords

  • Early-warning
  • Crowd stampede
  • Metro Station
  • Intelligent computing
  • Internet of things