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An agent based modeling approach to evaluate crowd movement strategies and density at bathing areas during Kumbh Mela-2019

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Abstract

Kumbh-Mela of Prayagraj, India, a festival of faith and belief, is one of the many significant gathering events worldwide. Pilgrims arrive from different places to take a holy bath at the confluence point of 3-rivers the Ganges, Yamuna, and Sarasvati. The police department is assigned a major role of handling and managing the dense traffic of pilgrims in this event to avoid unwanted situations. The primary surveillance points are the intersecting junctions and bathing zones. In addition, the authorities make crowd movement plans with different route diversion schemes and sets bathing time intervals to maintain crowd density at the Kumbh Mela site. Significantly, we must test these crowd management plans for a realistic assessment of population count, density maintenance, and time management. In this paper, we have created a model utilizing a micro-modeling agent-based approach that incorporates the virtual environment of the site. We have used AnyLogic, a ABMS tool, to incorporate social forces and stochastic behavior among the synthetic agents. The model simulates different crowd movement plans according to real behavioral scenarios. In the simulation, we have considered the whole bathing procedure as a halt time in the area. We have utilized our model to evaluate the time consumed by the pilgrims to reach “Ghat”. Also, to count the number of pilgrims that took a bath in 12 hours on different time intervals set for bathing. The significance of performing these evaluations is to assess the effect on density at the site during the whole arrival, bathing, and departure process.

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References

  1. Al-Kodmany K (2011) Planning for safety: the case of the symbolic stoning of the devil in hajj. J Archit Plan Res 28:28–43

    Google Scholar 

  2. Alsmirat M, Alalem F, Al-Ayyoub M, Jararweh Y, Gupta BB (2019) Impact of digital fingerprint image quality on the fingerprint recognition accuracy. Multimedia Tools and Applications 78:1–40. https://doi.org/10.1007/s11042-017-5537-5

    Article  Google Scholar 

  3. AlZéubi S, Shehab M, Al Ayyoub M, Jararweh Y, Gupta BB (2018) Parallel implementation for 3d medical volume fuzzy segmentation. Pattern Recogn Lett 130. https://doi.org/10.1016/j.patrec.2018.07.026

  4. Antonini G (2005) A discrete choice modeling framework for pedestrian walking behavior with application to human tracking in video sequences. Tech. rep., EPFL. https://doi.org/10.5075/epfl-thesis-3382

  5. Axelrod R (2006) Chapter 33 agent-based modeling as a bridge between disciplines. Handbook of Computational Economics, vol 2, Elsevier, pp 1565–1584. https://doi.org/10.1016/S1574-0021(05)02033-2

  6. Bonabeau E (2002) Agent-based modeling: Methods and techniques for simulating human systems. Proc Natl Acad Sci 99(suppl 3):7280–7287. https://doi.org/10.1073/pnas.082080899

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cai H, Rahman A, Su X, Zhang H (2014) A gis-microscopic simulation approach for optimizing road barrier placement and configuration in university campus emergency evacuation. International Journal of Disaster Resilience in the Built Environment 5(4):362–379. https://doi.org/10.1108/IJDRBE-06-2012-0014

    Article  Google Scholar 

  8. Chen M, Chen L, Miller-Hooks E (2007) Traffic signal timing for urban evacuation. Journal of Urban Planning and Development 133(1):30–42. https://doi.org/10.1061/(ASCE)0733-9488(2007)133:1(30)

    Article  Google Scholar 

  9. Chen X, Zhan FB (2008) Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies. Journal of the Operational Research Society 59(1):25–33. https://doi.org/10.1057/palgrave.jors.2602321

    Article  CAS  Google Scholar 

  10. Company A (2000) Anylogic 8.3, universal edition 2018. https://www.anylogic.com/

  11. Curtis S, Guy SJ, Zafar B, Manocha D (2011) Virtual tawaf: A case study in simulating the behavior of dense, heterogeneous crowds. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), IEEE, pp 128–135. https://doi.org/10.1109/ICCVW.2011.6130234

  12. Curtis S, Manocha D, Guy SJ, Zafar B (2013) Pedestrian velocity obstacles: Pedestrian simulation through reasoning in velocity space. PhD thesis, University of North Carolina at Chapel Hill. https://doi.org/10.17615/zbcj-xa48

  13. Daamen W, Hoogendoorn S (2012) Calibration of pedestrian simulation model for emergency doors by pedestrian type. Transp Res Rec 2316(1):69–75. https://doi.org/10.3141/2316-08

    Article  Google Scholar 

  14. Daamen W (1828) Hoogendoorn SP (2003) Experimental research of pedestrian walking behavior. Transp Res Rec 1:20–30. https://doi.org/10.3141/1828-03

    Article  Google Scholar 

  15. Dabra M, Gupta BB (2019) An efficient kp design framework of attribute-based searchable encryption for user level revocation in cloud. Concurrency and Computation: Practice and Experience 32:e5291. https://doi.org/10.1002/cpe.5291

    Article  Google Scholar 

  16. Dridi M (2015) Simulation of high density pedestrian flow: A microscopic model. Open Journal of Modelling and Simulation (OJMSi) 3:81–95. https://doi.org/10.4236/ojmsi.2015.33009

    Article  Google Scholar 

  17. Fruin JJ (1971) Designing for pedestrians: A level-of-service concept, vol 355. Highw. Res. Rec. 355. https://doi.org/10.1016/j.ascom.2016.03.003

  18. Fruin JJ (1993) The causes and prevention of crowd disasters. Engineering for crowd safety 1(10):99–108

    Google Scholar 

  19. Gulhare S, Verma A, Chakroborty P (2018) Comparison of pedestrian data of single file movement collected from controlled pedestrian experiment and from field in mass religious gathering. Collective Dynamics 3:1–14. https://doi.org/10.17815/CD.2018.16

  20. Haghighati R, Hassan A (2013) Modeling the flow of crowd during tawaf at masjid al-haram. Jurnal Mekanikal 36(1):2–18

    Google Scholar 

  21. Helbing D, Balietti S (2012) Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior, pp 25–70. https://doi.org/10.1007/978-3-642-24004-1_2

  22. Helbing D, Molnar P (1998) Social force model for pedestrian dynamics. Physical review E 51(5):4282. https://doi.org/10.1103/PhysRevE.51.4282

  23. Helbing D, Mukerji P (2012) Crowd disasters as systemic failures: analysis of the love parade disaster. EPJ Data Science 1(1):7. https://doi.org/10.1140/epjds7

    Article  Google Scholar 

  24. Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407(6803):487. https://doi.org/10.1038/35035023

    Article  ADS  CAS  PubMed  Google Scholar 

  25. Helbing D, Farkas IJ, Molnar P, Vicsek T (2002) Simulation of pedestrian crowds in normal and evacuation situations. Pedestrian and Evacuation Dynamics 21(2):21–58

    Google Scholar 

  26. Ijaz K, Sohail S, Hashish S (2015) A survey of latest approaches for crowd simulation and modeling using hybrid techniques. In: 17th UKSIMAMSS International Conference on Modelling and Simulation, pp 111–116

  27. Illiyas FT, Mani SK, Pradeepkumar A, Mohan K (2013) Human stampedes during religious festivals: A comparative review of mass gathering emergencies in india. Int J Disaster Risk Reduct 5:10–18. https://doi.org/10.1016/j.ijdrr.2013.09.003

    Article  Google Scholar 

  28. Ilyas QM (2013) A netlogo model for ramy al-jamarat in hajj. Journal of Basic and Applied Scientific Research 3(12):199–209

    Google Scholar 

  29. Jha M, Moore K (1886) Pashaie B (2004) Emergency evacuation planning with microscopic traffic simulation. Transp Res Rec 1:40–48. https://doi.org/10.3141/1886-06

    Article  Google Scholar 

  30. Johansson F (2013) Microscopic modeling and simulation of pedestrian traffic. PhD thesis, Linköping University Electronic Press. https://doi.org/10.3384/lic.diva-101085

  31. Kasthala S, Lakra HS (2015) Disaster preparedness for mass religious gatherings in india-learning from case studies. In: Second World Congress on Disaster Management, Visakhapatnam, Andhra Pradesh, India

  32. Kelley HH, Condry JC, Dahlke AE, Hill AH (1965) Collective behavior in a simulated panic situation. J Exp Soc Psychol 1(1):20–54. https://doi.org/10.1016/0022-1031(65)90035-1

    Article  Google Scholar 

  33. Kolli S, Karlapalem K (2013) Mama: Multi-agent management of crowds to avoid stampedes in long queues. In: Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems, International Foundation for Autonomous Agents and Multiagent Systems, vol 2, pp 1203–1204

  34. Kountouriotis V, Thomopoulos SC, Papelis Y (2014) An agent-based crowd behaviour model for real time crowd behaviour simulation. Pattern Recognition Letters 44:30–38. https://doi.org/10.1016/j.patrec.2013.10.024. pattern Recognition and Crowd Analysis

  35. Lakoba TI, Kaup DJ, Finkelstein NM (2005) Modifications of the helbing-molnar-farkas-vicsek social force model for pedestrian evolution. SIMULATION 81(5):339–352. https://doi.org/10.1177/0037549705052772

    Article  Google Scholar 

  36. Le Bon G (2002 [1895]) The crowd: A study of the popular mind (reprint edition). Mineola NY Dover Publications Inc

  37. Lee S, Son YJ (2009) Dynamic learning in human decision behavior for evacuation scenarios under bdi framework. In: Proceedings of the 2009 INFORMS Simulation Society Research Workshop. INFORMS Simulation Society: Catonsville, MD, pp 96–100

  38. Luna-Ramirez W, Fasli M (2018) Bridging the gap between abm and mas: A disaster-rescue simulation using jason and netlogo. Computers 7(2):24

    Article  Google Scholar 

  39. Macal C, North M (2010) Tutorial on agent-based modelling and simulation. J Simulation 4:151–162. https://doi.org/10.1057/jos.2010.3

    Article  Google Scholar 

  40. Mahmood I, Haris M, Sarjoughian H (2017) Analyzing emergency evacuation strategies for mass gatherings using crowd simulation and analysis framework: Hajj scenario. In: Proceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, Association for Computing Machinery, New York, NY, USA, SIGSIM-PADS ’17, pp 231–240. https://doi.org/10.1145/3064911.3064924

  41. Mao Y, Yang S, Li Z, Li Y (2020) Personality trait and group emotion contagion based crowd simulation for emergency evacuation. Multimedia Tools and Applications 79(5):3077–3104. https://doi.org/10.1007/s11042-018-6069-3

    Article  Google Scholar 

  42. McPhail C (2017) The myth of the madding crowd. Routledge. https://doi.org/10.4324/9781315133270

    Article  Google Scholar 

  43. Mulyana WW, Gunawan TS (2010) Hajj crowd simulation based on intelligent agent. In: International Conference on Computer and Communication Engineering (ICCCE’10), IEEE, pp 1–4

  44. Namoun A, Mir A, Alkhodre AB, Tufail A, Alrehaili A, Farquad M, Benaida M (2018) A multi-agent architecture for evacuating pilgrims in panic and emergency situations: The hajj scenario. J Theor Appl Inf Technol 96(20):6665–6676

    Google Scholar 

  45. Premkamal PK, Pasupuleti S, Pja A (2019) Efficient escrow-free cp-abe with constant size ciphertext and secret key for big data storage in cloud. International Journal of Cloud Applications and Computing 10:28–45. https://doi.org/10.4018/IJCAC.2020010103

    Article  Google Scholar 

  46. Railsback SF, Grimm V (2019) Agent-based and individual-based modeling: a practical introduction. https://doaj.org/article/13ba59b43b4b4545b9a5844a1edd490b/

  47. Rastogi R, Thaniarasu I, Chandra S (2010) Design implications of walking speed for pedestrian facilities. J Transp Eng 137(10):687–696. https://doi.org/10.1061/(asce)te.1943-5436.0000251

    Article  Google Scholar 

  48. Santos G, Aguirre BE (2004) A critical review of emergency evacuation simulation models

  49. Schadschneider A, Klingsch W, Klüpfel H, Kretz T, Rogsch C, Seyfried A (2009) Evacuation dynamics: Empirical results, modeling and applications. Encyclopedia of complexity and systems science pp 3142–3176. https://doi.org/10.1007/978-0-387-30440-3_187

  50. Shi X, Ye Z, Shiwakoti N, Li Z (2015) A review of experimental studies on complex pedestrian movement behaviors. In: CICTP 2015, American Society of Civil Engineers, Reston, VA, USA, pp 1081–1096. https://doi.org/10.1061/9780784479292.101

  51. 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. Accident Analysis & Prevention 95:405–416. https://doi.org/10.1016/j.aap.2015.10.009, traffic Safety in China: Challenges and Countermeasures

  52. Shiwakoti N, Shi X, Ye Z, Liu Y, Lin J (2016) A comparative study of pedestrian crowd flow at middle and corner exits. In: The 38th Australasian Transport Research Forum (ATRF 2016), vol 16-18, pp 1–10

  53. Smith ER, Conrey FR (2007) Agent-based modeling: A new approach for theory building in social psychology. Pers Soc Psychol Rev 11(1):87–104. https://doi.org/10.1177/1088868306294789

    Article  PubMed  Google Scholar 

  54. Still GK (2014) Introduction to crowd science, 1st edn. CRC Press

    Book  Google Scholar 

  55. Sumam MI, Vani K (2013) Agent based evacuation simulation using leader-follower model 4(8)

  56. Trivedi A, Pandey M (2020) Agent based modelling and simulation to estimate movement time of pilgrims from one place to another at allahabad jn. railway station during kumbh mela-2019. Autonomous Agents and Multi-Agent Systems 34(1):1–37. https://doi.org/10.1007/s10458-020-09454-x

  57. Vizzari G, Manenti L, Crociani L (2013) Adaptive pedestrian behaviour for the preservation of group cohesion. Complex Adaptive Systems Modeling 1(1):7. https://doi.org/10.1186/2194-3206-1-7

    Article  Google Scholar 

  58. Vizzari G, Manenti L, Ohtsuka K, Shimura K (2015) An agent-based pedestrian and group dynamics model applied to experimental and real-world scenarios. Journal of Intelligent Transportation Systems 19(1):32–45. https://doi.org/10.1080/15472450.2013.856718

    Article  Google Scholar 

  59. Wang H, Li Z, Li Y, Gupta BB, Choi C (2018) Visual saliency guided complex image retrieval. Pattern Recogn Lett 130. https://doi.org/10.1016/j.patrec.2018.08.010

  60. Wang P (2016) Understanding social-force model in psychological principles of collective behavior. arXiv preprint arXiv:1605.05146. https://doi.org/10.48550/arXiv.1605.05146

  61. Wang X, Li J (2013) Study on the simulation models for pedestrian evacuation movement. International Journal of Digital Content Technology and its Applications 7(8):503

    Article  Google Scholar 

  62. Wen KC (2013) A dynamic simulation of crowd flow in taipei railway and mrt station by multi-agent simulation system. Urban Planning and Design Research 1(4):59–68

    Google Scholar 

  63. Xi H, Lee S, Son YJ (2011) An integrated pedestrian behavior model based on extended decision field theory and social force model, Springer, London, pp 69–95. https://doi.org/10.1007/978-0-85729-883-6_4

  64. Yadav P, Maurya J, Singh A, Subodh (2019) Traffic Plan Manual (2019) Kumbh Mela. Bhargav Press

  65. Yi S, Li H, Wang X (2015) Pedestrian travel time estimation in crowded scenes. In: 2015 IEEE International Conference on Computer Vision (ICCV), IEEE, pp 3137–3145. https://doi.org/10.1109/ICCV.2015.359

  66. Yu C, Li J, Li X, Ren X, Gupta BB (2018) Four-image encryption scheme based on quaternion fresnel transform, chaos and computer generated hologram. Multimedia Tools and Applications 77. https://doi.org/10.1007/s11042-017-4637-6

  67. Zafar M, Zia K, Muhammad A, Ferscha A (2016) An agent-based model of crowd evacuation integrating agent perception and proximity pressure. In: Proceedings of the 14th International Conference on Advances in Mobile Computing and Multi Media, Association for Computing Machinery, MoMM ’16, pp 12–19. https://doi.org/10.1145/3007120.3007143

  68. Zou N, Yeh ST, Chang GL, Marquess A (1922) Zezeski M (2005) Simulation-based emergency evacuation system for ocean city, maryland, during hurricanes. Transp Res Rec 1:138–148. https://doi.org/10.1177/0361198105192200118

    Article  Google Scholar 

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Acknowledgements

This work has been supported by the Kumbh Mela Police, Kumbh Mela, Prayagraj, Uttar Pradesh. We especially want to acknowledge Mr. K.P. Singh, DIG, Kumbh Mela, Prayagraj, for time-to-time meeting with us, clearing our doubts, and allowing us to survey the site. We are also thankful to the reviewers for their constructive suggestions.

Funding

Ms. Abha Trivedi (Research Scholar, GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj) received a partial financial support from Kumbh Mela police authorities, Prayagraj. Dr. Mayank Pandey (Associate Professor, Computer Science, and Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Prayagraj) declares no conflicts of interest. The remaining authors have no conflicts of interest to declare.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP, India

    Abha Trivedi, Mayank Pandey & Rohan Chhabra

  2. Master of Management Studies (Public Policy), Indian Institute of Management Bangalore (IIMB), Bengaluru, India

    G. Ramesh

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Ms. Abha Trivedi has done this research work as a part of her PhD program with a partial support from Mr. Rohan Chhabra under the guidance of Dr. Mayank Pandey and Prof. G Ramesh.

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Correspondence to Abha Trivedi.

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Appendix Section 1: Density at Ghat

Appendix Section 1: Density at Ghat

Fig. 16
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Density at SNG when bathing time is 20 minutes

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Fig. 17
figure 17

Density at SNG when bathing time is 15 minutes

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Fig. 18
figure 18

Density at SNG when bathing time is 10 minutes

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Trivedi, A., Pandey, M., Ramesh, G. et al. An agent based modeling approach to evaluate crowd movement strategies and density at bathing areas during Kumbh Mela-2019. Multimed Tools Appl 83, 18739–18777 (2024). https://doi.org/10.1007/s11042-023-16267-z

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