Abstract
Kumbh Mela festival of India is one of the largest mass gathering event of huge religious importance all over the world. Large gatherings in these kind of religious events require rigorous monitoring and attention. Successful organization of such events requires synchronization among officials of different public departments such as police, health, security, communication, railways etc. The railway department plays a significant role in handling huge surge of passengers and their transportation during such events. Every 12-years Kumbh Mela is organized in the city of Prayagraj (formerly Allahabad) in northern India. The Allahabad Jn. railway station experiences huge inflow and outflow of pilgrims during Kumbh Mela. The railway authorities deploy predefined crowd movement strategies and boarding procedures for smooth transportation of pilgrims. However, these strategies are outlined based on previous experiences and ground knowledge of stakeholders. The strategies followed by railway authorities are needed to be evaluated and tested for realistic assessment and possible refinement before actual deployment. Our model is able to capture and simulate the real time behaviour of entities such as pilgrims and trains by programming them as synthetic agents. This model is helpful in analyzing the time taken by a group of pilgrims to move from a designated place to their target platform and board the train. The consumed time is calculated by simulating different movement and boarding procedures including the actual plans followed by the railway authorities. In this way it is possible to assess the efficiency of their movement plans and reasons about possible refinement.
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Abar, S., Theodoropoulos, G. K., Lemarinier, P., & OHare, G. M. (2017). Agent based modelling and simulation tools: A review of the state-of-art software. Computer Science Review, 24, 13–33.
Allan, R. J. (2010). Survey of agent based modelling and simulation tools. New York: Science & Technology Facilities Council.
Axelrod, R. (2006). Agent-based modeling as a bridge between disciplines. Handbook of Computational Economics, 2, 1565–1584.
Bandini, S., Manzoni, S., & Vizzari, G. (2009). Agent based modeling and simulation: An informatics perspective. Journal of Artificial Societies and Social Simulation, 12(4), 4.
Berryman, M. (2008). Review of software platforms for agent based models. Technical report. Defence Science and Technology Organisation Edinburgh (Australia) Land
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl. 3), 7280–7287.
Cao, K., Chen, Y., & Stuart, D. (2016). A fractional micro-macro model for crowds of pedestrians based on fractional mean field games. IEEE/CAA Journal of Automatica Sinica, 3(3), 261–270.
Cheng, H., & Yang, X. (2012). Emergency evacuation capacity of subway stations. Procedia-Social and Behavioral Sciences, 43, 339–348.
Cheung, C., & Lam, W. H. (1998). Pedestrian route choices between escalator and stairway in MTR stations. Journal of Transportation Engineering, 124(3), 277–285.
Chu, D., Wei, S., & Lin, Y. (2016). The application of pedestrian microscopic simulation technology in researching the influenced realm around urban rail transit station. Journal of Traffic and Transportation Engineering, 4, 242–246.
Company, A. (2000). Anylogic 8.3, universal edition 2018.
Cui, Q. L., Ichikawa, M., Kaneda, T., & Deguchi, H. (2011). Large scale crowd simulation of terminal station area when Tokai earthquake advisory information is announced officially. In Agent-based approaches in economic and social complex systems VI (pp. 161–174). Berlin: Springer.
Di Gangi, M., Russo, F., & Vitetta, A. (2003). A mesoscopic method for evacuation simulation on passenger ships: Models and algorithms. Pedestrian and Evacuation Dynamics, 2003, 197–208.
Ekyalimpa, R., Werner, M., Hague, S., AbouRizk, S., & Porter, N. (2016). A combined discrete-continuous simulation model for analyzing train-pedestrian interactions. In Proceedings of the 2016 winter simulation conference (pp. 1583–1594). New York: IEEE Press, WSC’16. http://dl.acm.org/citation.cfm?id=3042094.3042296.
Fernández, R., Valencia, A., & Seriani, S. (2015). On passenger saturation flow in public transport doors. Transportation Research Part A: Policy and Practice, 78, 102–112.
Fruin, J. J. (1993). The causes and prevention of crowd disasters. Engineering for Crowd Safety, 1(10), 99–108.
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.
Helbing, D. (1998). A fluid dynamic model for the movement of pedestrians. Preprint arXiv:cond-mat/9805213.
Helbing, D. (2012). Agent-based modeling. In D. Helbing (Eds.), Social self-organization. Understanding complex systems. Berlin, Heidelberg: Springer.
Helbing, D., & Johansson, A. (2009). Pedestrian, crowd and evacuation dynamics. Berlin: Springer.
Helbing, D., & Molnár, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51, 4282–4286. https://doi.org/10.1103/PhysRevE.51.4282.
Helbing, D., Farkas, I., & Vicsek, T. (2000). Simulating dynamical features of escape panic. Nature, 407(6803), 487.
Helbing, D., Farkas, I. J., Molnar, P., & Vicsek, T. (2002). Simulation of pedestrian crowds in normal and evacuation situations. Pedestrian and Evacuation Dynamics, 21(2), 21–58.
Hermant, L. (2007). Human movement behaviour in South African railway stations: Implications for design. In SATC 2011.
Hermant, L., & De Gersigny, M. (2010). Microscopic assessment of pedestrian space requirements within railway stations in South Africa. In SATC 2010.
Hermant, L., De Gersigny, M., Hermann, R., & Ahuja, R. (2010). Applying microscopic pedestrian simulation to the design assessment of various railway stations in South Africa. In Proceedings of the 29th Southern African transport conference (SATC 2010) (Vol. 16, p. 19).
Hughes, R. L. (2002). A continuum theory for the flow of pedestrians. Transportation Research Part B: Methodological, 36(6), 507–535.
Hughes, R. L. (2003). The flow of human crowds. Annual Review of Fluid Mechanics, 35(1), 169–182.
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).
Kachroo, P. (2009). Pedestrian dynamics: Mathematical theory and evacuation control. Boca Raton: CRC Press.
Kelley, H. H., Condry, J. C, Jr., Dahlke, A. E., & Hill, A. H. (1965). Collective behavior in a simulated panic situation. Journal of Experimental Social Psychology, 1(1), 20–54.
Kim, H., Kwon, S., Wu, S. K., & Sohn, K. (2014). Why do passengers choose a specific car of a metro train during the morning peak hours? Transportation Research Part A: Policy and Practice, 61, 249–258.
King, D., Srikukenthiran, S., & Shalaby, A. (2013). Using massmotion to analyze crowd congestion and mitigation measures at interchange subway stations: Case of Bloor–Yonge station in Toronto. In Annual meeting of the transportation research board. Toront: ARUP.
Lakoba, T. I., Kaup, D. J., & Finkelstein, N. M. (2005). Modifications of the Helbing–Molnar–Farkas–Vicsek social force model for pedestrian evolution. Simulation, 81(5), 339–352.
Macal, C. M., & North, M. J. (2005). Tutorial on agent-based modeling and simulation. In Proceedings of the 37th conference on winter simulation, winter simulation conference, WSC’05 (pp. 2–15).
Patange, P., & Bhakhtyapuri, V. (2017). Micro-simulation study on pedestrian flow at railway station. International Journal of Science Technology and Engineering, 3(09), 594–599.
Pettersson, P. (2011). Passenger waiting strategies on railway platforms-effects of information and platform facilities: Case study Sweden and Japan. Master of Science Thesis. Stockholm, Sweden: Royal Institute of Technology (KTH). https://doi.org/10.1017/CBO9781107415324.004.
Qingyan, D., Xifu, W., Qingchao, S., & Zhang, X. (2011). Modeling and simulation of rail transit pedestrian flow. Journal of Transportation Systems Engineering and Information Technology, 11(5), 99–106.
Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: A practical introduction. Princeton: Princeton University Press.
Rastogi, R., Thaniarasu, I., & Chandra, S. (2010). Design implications of walking speed for pedestrian facilities. Journal of Transportation Engineering, 137(10), 687–696.
Rindsfüser, G., & Klügl, F. (2007). Agent-based pedestrian simulation: A case study of the bern railway station. disP—The Planning Review, 43(170), 9–18.
Shah, J., Joshi, G., & Parida, P. (2013). Behavioral characteristics of pedestrian flow on stairway at railway station. Procedia-Social and Behavioral Sciences, 104, 688–697.
Smith, E. R., & Conrey, F. R. (2007). Agent-based modeling: A new approach for theory building in social psychology. Personality and Social Psychology Review, 11(1), 87–104.
Still, G. K. (2014). Introduction to crowd science. Boca Raton: CRC Press.
Tang, M., & Hu, Y. (2017). Pedestrian simulation in transit stations using agent-based analysis. Urban Rail Transit, 3(1), 54–60.
Teknomo, K., & Gerilla, G. P. (2008). Mesoscopic multi-agent pedestrian simulation. Transportation Research Trends, 1, 323–336.
Wang, P. (2016). Understanding social-force model in psychological principles of collective behavior. Preprint arXiv:1605.05146
Wang, W., Lo, S. M., Liu, S., & Ma, J. (2015). On the use of a pedestrian simulation model with natural behavior representation in metro stations. Procedia Computer Science, 52, 137–144.
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.
Wen, K. C. (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.
Zhang, Q., Han, B., & Li, D. (2008). Modeling and simulation of passenger alighting and boarding movement in Beijing metro stations. Transportation Research Part C: Emerging Technologies, 16(5), 635–649.
Acknowledgements
This Project is done as a part of research work by Abha Trivedi (Ph.D. Scholar, GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Allahabad) under the guidance of Dr. Mayank Pandey (Associate Professor, Computer Science and Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Prayagraj). This research is financially supported by the Allahabad division of North Central Railway (NCR), Prayagraj.
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Trivedi, A., Pandey, M. 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. Auton Agent Multi-Agent Syst 34, 30 (2020). https://doi.org/10.1007/s10458-020-09454-x
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DOI: https://doi.org/10.1007/s10458-020-09454-x