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Analiza zachowań tłumu w masowych zgromadzeniach
Języki publikacji
Abstrakty
Religious occasions, gathering at fairs and terminals, are the events of crowd gatherings. Such gatherings act as severe threats for crowds because of high density in less space, which ends up in adverse outcomes resulting in crowd stampedes. The movement of an individual person in a crowd is influenced by the physical factors. In the present study, characteristics like age, gender, group size, child holding, child carrying, people with luggage and without luggage are considered for crowd behaviour analysis. The average speed of the crowd movement was observed as 0.86 m/s. The statistical analysis concluded that there was a significant effect of age, gender, density and luggage on the crowd walking speed. Multi-linear regression (MLR) model was developed between crowd speed and significant factors observed from the statistical analysis. Location 1 data was used for the model development. This developed model was validated using Location 2 data. Gender has more significant effect on speed followed by luggage and age. This study helps in proper dispersal of crowd in a planned manner to that of diversified directional flow that exist during crowd gathering events.
Uroczystości religijne, targi czy zbiorowiska ludzkie na terminalach są przykładami masowych zgromadzeń. Stanowią one istotne zagrożenie dla tłumu ze względu na duże zagęszczenie ludzi na mniejszej przestrzeni, które przynosi negatywne efekty w postaci wzajemnego tratowania się. Na ruch poszczególnych osób w tłumie wpływają czynniki fizyczne. Analiza zachowań tłumu przedstawiona w niniejszym opracowaniu uwzględnia takie cechy jak wiek, płeć, wielkość grupy, trzymanie dziecka, niesienie dziecka, bagaż lub jego brak. Zaobserwowana średnia prędkość poruszania się tłumu wyniosła 0,86 m/s. Analiza statystyczna wykazała, że znaczący wpływ na prędkość, z jaką idzie tłum, miały wiek płeć, gęstość oraz bagaż. Model regresji wielolorakiej (MLR) został opracowany dla ujęcia związku pomiędzy prędkością a istotnymi czynnikami zaobserwowanymi w analizie statystycznej. Do opracowania modelu wykorzystano dane z lokalizacji 1. Opracowany model został zwalidowany z wykorzystaniem danych z lokalizacji 2. Płeć ma większy wpływ na prędkość niż odpowiednio bagaż oraz wiek. Niniejsze opracowanie pomaga we właściwym rozpraszaniu tłumu w zaplanowany sposób dostosowany do zróżnicowanego przepływu kierunkowego charakterystycznego dla masowych zgromadzeń.
Czasopismo
Rocznik
Tom
Strony
5--20
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
autor
- National Institute of Technology Warangal, Telangana State, India
autor
- National Institute of Technology Warangal, Telangana State, India
Bibliografia
- [1] Ali, S., M. Shah., 2007. A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1–6.
- [2] Andrade, E.L., Blunsden, S., Fisher, R.B., 2006. Modelling crowd scenes for event detection. In Proc. Int. Conf. Pattern Recognition, Washington, DC, pp. 175–178.
- [3] Boghossian, B.A., Velastin, S.A., 1999. Motion-based machine vision technique for the management of large crowds. In Proc. 6th IEEE Int. Conf. Electronics, Circuits and Systems, vol. 2, pp. 961–964.
- [4] Brostow, G.J., Cipolla, R., 2006. Unsupervised Bayesian detection of independent motion in crowds. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, Washington, DC, pp. 594–601.
- [5] Chan, A., Liang, Z., Vasconcelos, N., 2008. Privacy preserving crowd monitoring: Counting people without people models or tracking. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008, pp. 1–7.
- [6] Cheriyadat, A.M., Radke, R., 2008. Detecting dominant motions in dense crowds. In IEEE J. Select. Topics Signal Process. vol. 2, no. 4, pp. 568–581.
- [7] Davies, A.C., Yin, J.H., Velastin, S.A., 1995. Crowd monitoring using image processing. IEE Electron. Commun. Eng. J., vol. 7, no. 1, pp. 37–47.
- [8] Dridi, M.H., 2009. Tracking Individual Targets in High Density Crowd Scenes Analysis of a Video Recording in Hajj. In Current Urban Studies, 3, 35-53.
- [9] Faisel, T., Shibu, I., Pradeep kumar, K.M., Keshav Mohan, A.P., 2013. Human stampedes during religiousfestivals: A comparative review of mass gathering emergencies in India. International Journal of Disaster Risk Reduction, 5, 10-18.
- [10] Gayathri, H., Aparna, P.M., Ashish Verma, 2017. A review of studies on understanding crowd dynamics in the context of crowd safety in mass religious gatherings. International Journal of Disaster Risk Reduction, 25, 82-91.
- [11] Hong Bao., Wang, B., Yang, S., Lou, H., 2013. Crowd Density Estimation Based on Texture Feature Extraction. In journal of multimedia, vol. 8, no. 4.
- [12] http://www.opensourcephysics.org/items/detail.cfm?ID=7365, Tracker video analysis and modelling tool.
- [13] Jacobs, H., (1967). To count a crowd. Columbia Journalism Review 6, 36–40.
- [14] Jacques, J.C.S.Jr., Braun, A., Soldera, J., Musse, S.R., Jung, C.R., 2007. “Understanding people motion in video sequences using voronoi diagrams”. In Pattern Anal. Applicat., vol. 10, no. 4, pp. 321–332.
- [15] Jiang, M., Huang, J., Wang, X., Tang, J., Wu, C., 2014. An Approach for Crowd Density and Crowd Size Estimation.” In journal of software, vol. 9, no. 3.
- [16] Jun, Hu., Lei, You., Juan, Wei., Yangyong, Guo., Ying, Liang., 2014. The pedestrian evacuation model with collision probability in three-dimensional space Transportation Letters Vol. 6, Issue 4, 219-225.
- [17] Kong, D., Gray, D., Tao, H., 2006. A viewpoint invariant approach for crowd counting. In Proc. Int. Conf. Pattern Recognition, vol. 3, pp. 1187–1190.
- [18] Kratz, L., Nishino, K., 2009. Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1446–1453.
- [19] Leibe, E., Seemann, B., Schiele, B., 2005. Pedestrian detection in crowded scenes. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, Washington, DC, pp. 878–885.
- [20] Ma, R., Li, L., Huang, W., Tian, Q., 2004. On pixel count based crowd density estimation for visual surveillance. In Proc. IEEE Conf. Cybernetics and Intelligent Systems, vol. 1, pp. 170–173.
- [21] Marana, A., da Costa, L., Lotufo, R., Velastin, S., 1998. On the efficacy of texture analysis for crowd monitoring. In Proc. Int. Symp. Computer Graphics, Image Processing, and Vision (SIBGRAPI’98), Washington, DC, p. 354.
- [22] Mehran, R., Oyama, A., Shah, M., 2009. Abnormal crowd behaviour detection using social force model. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 935 942.
- [23] Meynberg, O., Cui, S., Reinartz, P., 2016. Detection of High-Density Crowds in Aerial Images Using Texture Classification. In Remote Sens. 8, 470.
- [24] Musse, S. R., Thalmann, D., 1997. A Model of Human Crowd Behaviour: Group Inter Relationship and Collision Detection Analysis. In Computer Animation and Simulations 97, Proc. Euro graphics workshop, Budapest, Springer Verlag, Wien, pp. 39-51.
- [25] Prasanna Kumar, G., Sumalini, T., 2015. Stampedes are Community Avertible Crowd Disasters. In second world conference on disaster management, Visakhapatnam, Andhra Pradesh, India, 19-22.
- [26] Rabaud V., Belongie, S., 2006. Counting crowded moving objects. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 705–711.
- [27] Rahmalan, H., Nixon, M., Carter, J., 2006. On crowd density estimation for surveillance. In Proc. Institution of Engineering and Technology Conf. Crime and Security, pp. 540–545.
- [28] Sankaran, M., Lakshmi, S., 2016. Method to determine pedestrian level of service for sidewalks in Indian context. Transportation Letters, Pages 1-8.
- [29] Wang, X., Ma, X., Grimson, W.E.L., 2009. Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. In IEEE Trans. Pattern Anal. Machine Intell., vol. 31, no. 3, pp. 539–555.
- [30] Wolf, P.R., Dewitt, B.A., 2000. Elements of photogrammetry with applications in GIS, Third edition. McGraw Hil.
- [31] Wu, X., Liang, G., Lee, K.K., Xu, Y., 2006. Crowd density estimation using texture analysis and learning. In Proc. IEEE Int. Conf. Robotics and Biomimetics, pp. 214–219.
- [32] Zhao, T., Nevatia, R., 2003. Bayesian human segmentation in crowded situations. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 459–466.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2d3baa29-bdc7-493d-b6a4-792253f47a90