Occurrence mechanism and coping paths of accidents of highly aggregated tourist crowds based on system dynamics

English version

Autoři: Jie Yin ^aff001; Xiang-min Zheng ^aff001; Ruey-Chyn Tsaur ^aff002
Působiště autorů: College of tourism, Huaqiao University, Quanzhou, Fujian Province, China ^aff001; Department of Management Sciences, Tamkang University, New Taipei City, Taiwan ^aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222389

Souhrn

The safety of highly aggregated tourist crowds is a challenging and important issue. This paper not only provided a comprehensive analysis of the accidents of highly aggregated tourist crowds but also determined the occurrence mechanism and coping paths. Based on the analysis of multiple cases, we found that the variable status of highly aggregated tourist crowds was the result of the interaction of three main elements: multisource pressure, state mutations and management responses. A series of factors interact and result in accidents, and the lack of a management response or a low-quality management response is the root cause of such accidents. A high-quality management response is a basic safety precaution for highly aggregated tourist crowds. Therefore, forming a virtuous circle of multisource pressure, state mutations and management responses is an effective path for coping with accidents.

Klíčová slova:

Biology and life sciences – Physical sciences – Chemistry – Engineering and technology – Psychology – Social sciences – Sociology – Communications – Social communication – Computer and information sciences – Network analysis – Mathematics – Behavior – Equipment – Earth sciences – Ecology and environmental sciences – Marine and aquatic sciences – Sports science – Bodies of water – Aquatic environments – Freshwater environments – Social networks – Laboratory equipment – Filter paper – Recreation – Sports – Systems science – Control engineering – Anthropology – Cultural anthropology – Religion – Social media – Control theory – Psychological defense mechanisms – Rivers – Catalysis

Zdroje

1. Zhan B, Monekosso DN, Remagnino P, Velastin SA, Xu L-Q (2008) Crowd analysis: a survey. Machine Vision and Applications 19: 345–357.

2. Johansson A, Batty M, Hayashi K, Al Bar O, Marcozzi D, Memish Z. (2012) Crowd and environmental management during mass gatherings. The Lancet Infectious Diseases 12: 150–156. doi: 10.1016/S1473-3099(11)70287-0 22252150

3. Alnabulsi H, Drury J (2014) Social identification moderates the effect of crowd density on safety at the Hajj. Proc Natl Acad Sci USA 111: 9091–9096. doi: 10.1073/pnas.1404953111 24927593

4. Reicher SD (1996) The Battle of Westminster’:Developing the social identity model of crowd behaviour in order to explain the initiation and development of collective conflict. European Journal of Social Psychology 26: 115–134.

5. Novelli D, Drury J, Reicher S, Stott C (2013) Crowdedness mediates the effect of social identification on positive emotion in a crowd: A survey of two crowd events. PloS one 8: e78983. doi: 10.1371/journal.pone.0078983 24236079

6. Stott C, Adang O. L A., Schreiber M (2007) Variability in the collective behaviour of England fans at Euro2004 ‘Hooliganism’, public order policing and social change. European journal of social psychology 37: 75–100.

7. Cassidy C, Hopkins N, Levine M, Pandey J, Reicher S, Singh P (2007) Social identity and collective behaviour: Some lessons from Indian research at the Magh Mela at Prayag. Psychological Studies-University of Calicut 52: 286.

8. Neville F, Reicher S (2011) The experience of collective participation: Shared identity, relatedness, and emotionality. Contemporary Social Science 6: 377–396.

9. Popp M (2012) Positive and Negative Urban Tourist Crowding: Florence, Italy. Tourism Geographies 14: 50–72.

10. Shi B, Zhao J, Chen P-J (2017) Exploring urban tourism crowding in Shanghai via crowdsourcing geospatial data. Current Issues in Tourism 20: 1186–1209.

11. Zachko O, Golovatyi R, Yevdokymova A (2017) Development of a simulation model of safety management in the projects for creating sites with mass gathering of people. Eastern-European Journal of Enterprise Technologies 86: 15–24.

12. Khozium MO, Abuarafah AG, AbdRabou E (2012) A proposed computer-based system architecture for crowd management of pilgrims using thermography. Life Science Journal 9: 377–383.

13. Mehdi M, Dirk H, Guy T (2011) How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences 108: 6884–6888.

14. Simon A (2017) Crowd planning for public safety. Perspectives in Public Health 137: 25–28. doi: 10.1177/1757913916681265 28074698

15. Zhang XL, Weng WG, Yuan HY, Chen JG (2013) Empirical study of a unidirectional dense crowd during a real mass event. Physica A: Statistical Mechanics and its Applications 392: 2781–2791.

16. Pin SC, Haron F, Sarmady S, Talib AZ, Khader AT (2011) Applying TRIZ principles in crowd management. Safety Science 49: 286–291.

17. Saad A, H.,Algadhi, Hani S, Mahmassani (1990) Modelling Crowd Behavior and Movement Application to Makkah Pilgrimage. Transportation and traffic theory: 59–78.

18. Shelby B, Vaske J, J. (1991) Using Normative Data to Develop Evaluative Standards for Resource Management: A Comment on Three Recent Papers. Journal of Leisure Research 23: 173.

19. Lee RS, Hughes RL (2005) Exploring trampling and crushing in a crowd. Journal of transportation engineering 131: 575–582.

20. Lee RS, Hughes RL (2006) Prediction of human crowd pressures. Accid Anal Prev 38: 712–722. doi: 10.1016/j.aap.2006.01.001 16487473

21. Karpagavalli P, Ramprasad AV (2013) Estimating the density of the people and counting the number of people in a crowd environment for human safety. International conference on Communication and Signal Processing. pp. 663–667.

22. Anders J, Dirk H (2008) From Crowd Dynamics to Crowd Safety:A Video-Based Analysis. Advances in Complex Systems 11: 497–527.

23. Angela TYH, Viswanathan V, Lees M, Cai W (2014) Analysing the Effectiveness of Wearable Wireless Sensors in Controlling Crowd Disasters. Procedia Computer Science 29: 1590–1599.

24. Rahmalan H, Nixon MS, Carter JN (2006) On crowd density estimation for surveillance. IET Conference on Crime and Security. pp. 540–545.

25. Yuan Y, Qiu C, Xi W, Zhao J (2011) Crowd Density Estimation Using Wireless Sensor Networks. 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks. pp. 138–145.

26. Shi X, Ye Z, Shiwakoti N, Tang D, Wang C, Wang W (2016) Empirical investigation on safety constraints of merging pedestrian crowd through macroscopic and microscopic analysis. Accid Anal Prev 95: 405–416. doi: 10.1016/j.aap.2015.10.009 26519346

27. Alfredo C, Emanuele Z (2006) Analysis of Jumping Crowd on Stadium Stands Through Image Processing to Security Purposes. IEEE International Workshop on Measurement Systems for Homeland Security, Contraband Detection and Personal Safety: 56–61.

28. Rabiee H, Mousavi H, Nabi M, Ravanbakhsh M (2018) Detection and localization of crowd behavior using a novel tracklet-based model. International Journal of Machine Learning and Cybernetics 9: 1999–2010.

29. Manoj K, Charul B (2017) Hybrid tracking model and GSLM based neural network for crowd behavior recognition. Journal of Central South University 24: 2071–2081.

30. Gu X, Cui J, Zhu Q (2014) Abnormal crowd behavior detection by using the particle entropy. Optik 125: 3428–3433.

31. Xiong G, Cheng J, Wu X, Chen Y-L, Ou Y, Xu Y (2012) An energy model approach to people counting for abnormal crowd behavior detection. Neurocomputing 83: 121–135.

32. Zhao H, Wang S, Xu S, Zhang Y, Sugisaka M (2017) A Method of Detecting Abnormal Crowd Behavior Events Applied in Air Patrol Robot. Journal of Robotics, Networking and Artificial Life 4: 77–80.

33. Ivancevic VG, Reid DJ, Aidman EV (2010) Crowd behavior dynamics: entropic path-integral model. Nonlinear Dynamics 59: 351–373.

34. Zhao Y, Yuan M, qi, Su G, feng, Chen T (2015) Crowd Security Detection based on Entropy Model. the ISCRAM 2015 Conference. pp. 24–33.

35. Liu T, Liu Z, Ma M, Chen T, Liu C, et al. (2018) 3D visual simulation of individual and crowd behavior in earthquake evacuation. Simulation 95: 65–81.

36. Yuan Z, Guo R, Tang S, He B, Bian L, Li Y (2019) Simulation of the Separating Crowd Behavior in a T-Shaped Channel Based on the Social Force Model. IEEE Access: 13668–13682.

37. Sikora W, Malinowski J (2013) Crowd Behavior as an Example of the Evolution of a Complex System—Evacuation Models Proposal Based on the Symmetry Analysis Approach. Acta Physica Polonica A 124: 1005–1012.

38. Moussaıd M, Kapadia M, Thrash T, Sumner RW, Gross M, Helbing D, et al. (2016) Crowd behaviour during high-stress evacuations in an immersive virtual environment. Journal of The Royal Society Interface 13.

39. Yin J, Zheng X, Dong B, Jiao N (2016) Tourists crowded places: concept, characteristics, risks and research topics. Journal of Chongqing Technology and Business University(Social Science Edition): 34–41.

40. Yin J, Zheng X (2017) The Research on the Safety Risk of Tourists Crowded Places Which is Based on the Optimal Dimension Analysis. Science Economy Society: 76–82.

41. Wang Y, Li Q, Chen J (2011) Simulation of Congestion Alleviating Strategies in Sightseeing Public Places. China Safety Science Journal: 27–32.

42. Ren J, Zheng W (2013) Dynamic real-time scheduling simulation of tourist routes in the peak tourist area. Statistics and Decision: 42–45.

43. Zhang Y, Chen M (2014) Research on rapid evacuation method of tourist in crowded areas. Computer Simulation: 432–435.

44. Helbing D, Mukerji P (2012) Crowd disasters as systemic failures:analysis of the Love Parade disaster. EPJ Data Science 1: 1–40.

45. Sharma D, Bhondekar AP, Shukla AK, Ghanshyam C (2018) A review on technological advancements in crowd management. Journal of Ambient Intelligence and Humanized Computing 9: 485–495.

46. Shen D, Zhang W, Xiong X, Li X, Zhang Y (2016) Trading and non-trading period Internet information flow and intraday return volatility. Physica A: Statistical Mechanics and its Applications 451: 519–524.

47. Song P, Zhang C, Xu Y, Huang L (2010) Brand extension of online technology products: Evidence from search engine to virtual communities and online news. Decision Support Systems 49: 91–99.

48. Wang J, Fu Z, Cheng J, Ma F (2010) The Measurement of Relative Recall with Weights: a Perspective of User Feedback. Journal of Computers 5.

49. Qiu L, Lu J, Yang S, Qu W, Zhu T (2015) What does your selfie say about you? Computers in Human Behavior 52: 443–449.

50. Xu Z, Zhang H, Sugumaran V, Choo K-KR, Mei L, Zhu Y (2016) Participatory sensing-based semantic and spatial analysis of urban emergency events using mobile social media. EURASIP Journal on Wireless Communications and Networking: 44.

51. Forrester JW (1961) Industrial Dynamics. Cambridge: MIT Press.

52. Lu Y, Zhang S-G, Hao L, Huangfu H-Y, Sheng H (2016) System dynamics modeling of the safety evolution of blended-wing-body subscale demonstrator flight testing. Safety Science 89: 219–230.

53. Goh YM, Love PED (2012) Methodological application of system dynamics for evaluating traffic safety policy. Safety Science 50: 1594–1605.

54. Bouloiz H, Garbolino E, Tkiouat M, Guarnieri F (2013) A system dynamics model for behavioral analysis of safety conditions in a chemical storage unit. Safety Science 58: 32–40.

55. Goh YM, Love PED, Brown H, Spickett J (2012) Organizational Accidents: A Systemic Model of Production versus Protection. Journal of Management Studies 49: 52–76.

56. Zhao B, Tang T, Ning B (2017) System dynamics approach for modelling the variation of organizational factors for risk control in automatic metro. Safety Science 94: 128–142.

57. Shin M, Lee HS, Park M, Moon M, Han S (2014) A system dynamics approach for modeling construction workers’ safety attitudes and behaviors. Accid Anal Prev 68: 95–105. doi: 10.1016/j.aap.2013.09.019 24268437

58. Lane DC, Monefeldt C, Rosenhead JV (2016) Looking in the wrong place for healthcare improvements:A system dynamics study of an accident and emergency department. Operational Research for Emergency Planning in Healthcare 2: 92–121.

59. Garbolino E, Chery JP, Guarnieri F (2016) A Simplified Approach to Risk Assessment Based on System Dynamics:An Industrial Case Study. Risk Analysis 36: 16–29. doi: 10.1111/risa.12534 26765309

60. Davies JC, Stevens G, Manningc DP (1998) Understanding accident mechanisms: an analysis of the components of 2516 accidents collected in a MAIM database. Safety Science 29: 25–58.

61. Cooke DL (2003) A system dynamics analysis of the Westray mine disaster. System Dynamic Review 19: 139–166.

62. Marais K, Saleh JH, Leveson NG (2006) Archetypes for organizational safety. Safety Science 44: 565–582.

63. Jeon JW, Wang Y, Yeo GT (2016) Ship Safety Policy Recommendations for Korea: Application of System Dynamics. The Asian Journal of Shipping and Logistics 32: 73–79.

64. Bellomo N, Clarke D, Gibelli L, Townsend P, Vreugdenhil B, J (2016) Crowd dynamics and safety Reply to comments on “Human behaviours in evacuation crowd dynamics: From modelling to “big data” toward crisis management”. Physics of Life Review 18: 55–65.

65. Yin J, Zheng X, min (2018) Application of Grounded Theory to Identify Factors Influencing the Security of Highly Aggregated Tourist Crowds and their Implementation Paths. Tourism Tribune 33: 133–144.

66. Dirk H, Anders J, Al-Abideen Habib Z (2007) The Dynamics of Crowd Disasters: An Empirical Study. Physical review E 75: 1–7.

67. Eberlein Robert L, Peterson David W (1992) Understanding models with Vensim. European Journal of Operational Research 59: 216–219.

68. Alaska YA, Aldawas AD, Aljerian NA, Memish ZA, Suner S (2017) The impact of crowd control measures on the occurrence of stampedes during Mass Gatherings: The Hajj experience. Travel Med Infect Dis 15: 67–70. doi: 10.1016/j.tmaid.2016.09.002 27640116

69. Fradi H, Dugelay J-L (2015) Spatial and temporal variations of feature tracks for crowd behavior analysis. Journal on Multimodal User Interfaces 10: 307–317.

70. Wang B, Ye M, Li X, Zhao F, Ding J (2011) Abnormal crowd behavior detection using high-frequency and spatio-temporal features. Machine Vision and Applications 23: 501–511.

71. Wang B, Ye M, Li X, Zhao F (2011) Abnormal crowd behavior detection using size-adapted spatio-temporal features. International Journal of Control, Automation and Systems 9: 905–912.

72. Zeitz Kathryn M., Tan HM, Zeitz CJ (2009) Crowd Behavior at Mass Gatherings: A Literature Review. Prehospital and disaster medicine 24: 32–38. 19557955

Occurrence mechanism and coping paths of accidents of highly aggregated tourist crowds based on system dynamics

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