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


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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. 1.

    Carley KM, Malik M, Landwehr PM et al (2016) Crowd sourcing disaster management: the complex nature of twitter usage in Padang Indonesia. Saf Sci 90:48–61

    Article  Google Scholar 

  2. 2.

    Castillo-Manzano JI, López-Valpuesta L (2009) Urban retail fabric and the metro: a complex relationship. Lessons from middle-sized Spanish cities. Cities 26(3):141–147

    Article  Google Scholar 

  3. 3.

    Flamini M, Pacciarelli D (2008) Real time management of a metro rail terminus. Eur J Oper Res 189(3):746–761

    Article  Google Scholar 

  4. 4.

    Haghani M, Sarvi M (2018) Crowd behaviour and motion: empirical methods. Transp Res B Methodol 107:253–294

    Article  Google Scholar 

  5. 5.

    Helbing D (2001) Traffic and related self-driven many-particle systems. Rev Mod Phys 73(4):1067–1141

    MathSciNet  Article  Google Scholar 

  6. 6.

    Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407(6803):487–490

    Article  Google Scholar 

  7. 7.

    Henein CM, White T (2007) Macroscopic effects of microscopic forces between agents in crowd models. Physica A 373:694–712

    Article  Google Scholar 

  8. 8.

    Ishizaka A (2012) A multicriteria approach with AHP and clusters for the selection among a large number of suppliers. Pesquisa Oper  32(1):1–15

  9. 9.

    Ishizaka A, Pearman C, Nemery P (2012) AHPSort: an AHP-based method for sorting problems. Int J Prod Res 50(17):4767–4784

    Article  Google Scholar 

  10. 10.

    Kirchner A, Schadschneider A (2002) Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Physica A 312(1-2):260–276

    Article  Google Scholar 

  11. 11.

    Lee RSC, Hughes RL (2005) Exploring stampede and crushing in a crowd. J Transp Eng-Asce 131(8):575–582

    Article  Google Scholar 

  12. 12.

    Lee RS, Hughes RL (2006) Prediction of human crowd pressures. Accid Anal Prev 38(4):712–722

    Article  Google Scholar 

  13. 13.

    Li Q, Dou R, Chen F et al (2014) A QoS-oriented web service composition approach based on multi-population genetic algorithm for internet of things. Int J Comput Int Sys 7(sup2):26–34

    Article  Google Scholar 

  14. 14.

    Li J, Wang L, Tang S, Zhang B, Zhang Y (2016) Risk-based crowd massing early warning approach for public places: a case study in China. Saf Sci 89:114–128

    Article  Google Scholar 

  15. 15.

    Li C, Qin J, Li J, Hou Q (2016) The accident early warning system for iron and steel enterprises based on combination weighting and Grey prediction model GM (1,1). Saf Sci 89:19–27

    Article  Google Scholar 

  16. 16.

    Lian L, Mai X, Song W, Richard YKK, Rui Y, Jin S (2017) Pedestrian merging behavior analysis: an experimental study. Fire Saf J 91:918–925

    Article  Google Scholar 

  17. 17.

    Lian L, Song W, Richard YKK, Ma J, Telesca L (2017) Long-range dependence and time-clustering behavior in pedestrian movement patterns in stampedes: the love parade case-study. Physica A 469:265–274

    Article  Google Scholar 

  18. 18.

    Lin J, Lo C (2008) Valuing user external benefits and developing management strategies for metro system underground arcades. Tunn Undergr Space Technol 23(2):103–110

    Article  Google Scholar 

  19. 19.

    Miccoli F, Ishizaka A (2017) Sorting municipalities in Umbria according to the risk of wolf attacks with AHPSort II. Ecol Indic 73:741–755

    Article  Google Scholar 

  20. 20.

    Tang XF, Niu XZ, Ali S (2014) Research on energy-aware topology strategy based on wireless sensor in internet of things. Int J Comput Int  Sys 7(6):1137–1147

    Article  Google Scholar 

  21. 21.

    Teknomo K (2002) Microscopic pedestrian flow characteristics development of an image processing data collection and simulation model. Tohoku University, Japan

    Google Scholar 

  22. 22.

    Wang L, Rodriguez RM, Wang Y (2018) A dynamic multi-attribute group emergency decision making method considering experts’ hesitation. Int J  Comput Int Sys 11(1):163–182

    Article  Google Scholar 

  23. 23.

    Xie K (2016) Early warning system for crowd stampede. 2016 2nd international conference on industrial informatics - computing technology, intelligent technology, industrial information integration (Iciicii), pp 1–4

  24. 24.

    Yang J, He S, Lin Y et al (2017) Multimedia cloud transmission and storage system based on internet of things. Multimed Tools  App 1–16

  25. 25.

    Zhang J, Yao D (2010) Intelligent pedestrian flow monitoring systems in shopping areas. International symposium on information engineering and electronic commerce. IEEE, pp 1–4

  26. 26.

    Zhao Z, Liang D (2016) Pedestrian flow characteristic of Metro Station along with the mall. Procedia Eng 135:602–606

    Article  Google Scholar 

  27. 27.

    Zhao Z, Yan J, Liang D, Ye S (2014) Pedestrian flow characteristic of typical Metro Station near the commercial property. Procedia Eng 71:81–86

    Article  Google Scholar 

  28. 28.

    Zhao Y, Lu T, Li M, Tian L (2017) The self-slowing behavioral mechanism of pedestrians under normal and emergency conditions. Phys Lett A 381(37):3149–3160

    Article  Google Scholar 

  29. 29.

    Zhou J, Pei H, Haishan Wu (2018) Early warning of human crowds based on query data from Baidu map analysis based on Shanghai stampede. Big data support of urban planning and management, pp 19–41

Download references


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

Author information



Corresponding author

Correspondence to Yanlan Mei.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation


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