Big Data Framework for Crowd Monitoring in Large Crowded Events
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The management of large events with hundreds of thousands of individuals has remained a challenge over the years. Crushes and stampedes occurring in the events of mass gathering have swallowed many valuable lives around the world. Considering the substantial advancement in positional tracking, wearable technology, and wireless communication, many event organizers are embracing the use of these technologies to get assistance in managing large events. Intelligent monitoring of crowd movement and timely analysis of evolving conditions may aid in early detection of critical situations. The current research aims to propose a big data resource framework to model, simulate, and visualize the crowd conditions for actual venue settings. A distributed framework has been presented to monitor the movement and interaction of individuals in large crowded events through localized sensing and geospatial analysis of massive positional data. The pilgrimage (Hajj) has been considered as a case study for demonstrating the effectiveness of the proposed framework. The proposed framework has been with the help of synthetic data that covered some useful and frequent scenarios based on the case study of pilgrimage (hajj), which is an annual event involving more than a million people.
U. Blanke, G. Troster, T. Franke, and P. Lukowicz, “Capturing crowd dynamics at large scale events using participatory GPS-localization,” presented at IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). Singapore, Apr. 21–24, 2014.
M. Versichele, T. Neutens, M. Delafontaine, and N. Van de Weghe, “The use of bluetooth for analysing spatiotemporal dynamics of human movement at mass events: A case study of the ghent festivities,” Appl. Geogr., vol. 32, no. 2, pp. 208–220, Mar. 2012, doi: https://doi.org/ 10.1016/j.apgeog.2011.05.011
J. E. Almeida, R. J. Rosseti, and A. L. Coelho, “Crowd simulation modeling applied to emergency and evacuation simulations using multi-agent systems,” arXiv:1303.4692, 2013, doi: https://doi.org/10.48550/arXiv.1303.4692
N. Pelechano, J. M. Allbeck, and N. I. Badler, “Controlling individual agents in high-density crowd simulation,” presented at Center for Human Modeling and Simulation, Aug. 3, 2007.
B. Ulicny and D. Thalmann, “Crowd simulation for interactive virtual environments and VR training systems,” in Computer Animation and Simulation, N. Magnenat-Thalmann and D. Thalmann, Eds., Vienna, Austria: Spring, 2001, pp. 163–170.
T. Weiss, A. Litteneker, C. Jiang, and D. Terzopoulos, “Position-based real-time simulation of large crowds,” Comput. Graph., vol. 78, pp. 12–22, Feb. 2019, doi: https://doi.org/10.1016 /j.cag.2018.10.008
S.-K. Wong, Y.-H. Chou, and H.-Y. Yang, “A framework for simulating agent-based cooperative tasks in crowd simulation,” in Proc. ACM SIGGRAPH Symp. Interact. 3D Graph. Games, 2018, pp. 1–10, doi: https:// doi.org/10.1145/3190834.3190839
A. Trivedi and S. Rao, “Agent-based modeling of emergency evacuations considering human panic behavior,” IEEE Transact. Comput. Social Syst., vol. 5, no. 1, pp. 277–288, Jan. 2018, https://doi.org/10.1109/TCSS.2017.2783332
I. Karamouzas, N. Sohre, R. Hu, and S. J. Guy, “Crowd space: A predictive crowd analysis technique,” ACM Transac. Graph., vol. 37, no. 6, pp. 1–14, Dec. 2018, doi: https://doi.org /10.1145/3272127.3275079
S. Zheng and H. Liu, “Improved multi-agent deep deterministic policy gradient for path planning-based crowd simulation,” IEEE Access, vol. 7, pp. 147755–147770, Oct. 2019, doi: https://doi.org/10.1109/ACCESS.2019.2946659
Z. Fa, W. Songchun, and S. Zhihua, “Evacuation during violent attacks: Agent-based modeling and simulation,” presented at IEEE 9th International Conference on Software Engineering and Service Science, Beijing, China, Nov. 23–25, 2018.
Q. Wang, H. Liu, K. Gao, and L. Zhang, “Improved multi-agent reinforcement learning for path planning-based crowd simulation,” IEEE Access, vol. 7, pp. 73841–73855, June 2019, doi: https://doi.org/ 10.1109/ACCESS.2019.2920913
S. Kim, A. Bera, A. Best, R. Chabra, and D. Manocha, “Interactive and adaptive data-driven crowd simulation,” presented at 2016 IEEE Virtual Reality (VR), Greenville, SC, USA, Mar. 19–23, 2016.
N. A. Nawaz, N. S. Alghamdi, H. Karamti, and M. A. Khan, “An intelligent cluster verification model using wsn to avoid close proximity and control outbreak of pandemic in a massive crowd,” Comput. Model. Eng. Sci., 2022, doi: https://doi.org/10. 32604/cmes.2022.020791
N. A. H. Nawaz, H. R. Malik, A. J. Alshaor, and K. Abid, “A simulation based proactive approach for smart capacity estimation in the context of dynamic positions and events,” Adv. Sci. Technol. Eng. Syst. J., vol. 5, no. 6, pp. 423–438, Nov. 2020, doi: https://dx.doi.org/10.25046/aj050651
N. A. Nawaz, A. Waqas, Z. M. Yusof, and A. Shah, “A framework for smart estimation of demand-supply for crowdsource management using WSN,” in Proc. Second Int. Conf. Inter Things Data Cloud Comput., Mar. 2017, pp. 1–5, doi: https://doi.org/ 10.1145/3018896.3025140
G. Lu, L. Chen, and W. Luo, “Real-time crowd simulation integrating potential fields and agent method,” ACM Transac. Mod. Comput. Simulat., vol. 26, no. 4, pp. 1–16, Mar. 2016, doi: https://doi.org/10.1145/2885496
L. Hong, J. Gao, and W. Zhu, “Self-evacuation modelling and simulation of passengers in metro stations,” Saf. Sci., vol. 110, pp. 127–133, Dec. 2018, doi: https://doi.org/10.1016/j.ssci. 2018.05.013
Y. Yuan, “Crowd monitoring using mobile phones,” presented at Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China, Aug. 26–27, 2014, doi: https://doi.org/10. 1109/IHMSC.2014.71
K. H. Cheong, et al., “Practical automated video analytics for crowd monitoring and counting,” IEEE Access, vol. 7, pp. 183252–183261, Dec. 2019, doi: https://doi.org/ 10.1109/ACCESS.2019.2958255
H. Hong, G. D. De Silva, and M. C. Chan, “Crowdprobe: Non-invasive crowd monitoring with wi-fi probe,” in Proc. ACM Interact. Mobil Wear Ubiquit Technol., vol. 2, no. 3, pp. 1–23, Sep. 2018, doi: https://doi.org /10.1145/3264925
N. A. Nawaz, A. Waqas, Z. M. Yusof, A. W. Mahesar, S. and A, Shah, “WSN based sensing model for smart crowd movement with identification: An extended study,” J. Theoret. Appl. Inform. Technol., vol. 95, no. 5, 2017.
N. S. Alghamdi, M. A. Khan, H. Karamti, and N. A. Nawaz, “Internet of Things (IoT) enabled smart queuing model to support massive safe crowd at Ka’aba,” Alexand Eng. J., vol. 62, no. 12, Dec. 2022, doi: https://doi.org/10. 1016/j.aej.2022.06.053
J. Weppner, B. Bischke, and P. Lukowicz, “Monitoring crowd condition in public spaces by tracking mobile consumer devices with wifi inter-face,” in Proc. ACM Int. Joint Conf. Pervasive Ubiquit Comput: Adjunct, Sep. 2016, pp. 1363–1371, doi: https://doi.org/10.1145/2968219. 2968414
T. Kulshrestha, D. Saxena, R. Niyogi, and J. Cao, “Real-time crowd monitoring using seamless indoor-outdoor localization,” IEEE Transac. Mob. Comput., vol. 19, no. 3, pp. 664–679, Feb. 2019, doi: https://doi.org /10.1109/tmc.2019.2897561
F. Wu, M. Zhu, Q. Wang, X. Zhao, W. Chen, and R. Maciejewski, “Spatial–temporal visualization of city-wide crowd movement,” J. Visualiz., vol. 20, no. 2, pp. 183–194, 2017, doi: https://doi.org/10.1007/s12650-016-0368-4
T. H. Noor, “Behavior analysis-based iot services for crowd management,” Comput. J., June 2022, doi: https://doi. org/10.1093/comjnl/bxac071
X.-C. Liao, W.-N. Chen, X.-Q. Guo, J. Zhong, and X.-M. Hu, “Crowd management through optimal layout of fences: An ant colony approach based on crowd simulation,” IEEE Transac. Intell. Transpor. Syst., 2023, doi: https://doi.org/10.1109/tits.2023.3272318
A. Hamrouni, “Socially connected internet-of-things devices for crowd management systems,” M.S. thesis, Comput. Elec. Mathemat. Sci. Eng. Divis., King Abdullah Univ. Sci. Technol, 2023.
P. Knob, V. F. de Andrade Araujo, R. M. Favaretto, and S. R. Musse, “Visualization of interactions in crowd simulation and video sequences,” in 17th Braz. Symp. Comput. Games Digi. Entertain. Oct. 29–Nov. 1, 2018, pp. 250–25, doi: https://doi.org/10. 1109/SBGAMES.2018.00037
S. N. Saeed, A. Abid, E. U. Waraich, S. Atta, A. Naseer, A. A. Sheikh, and E. Felemban, “iCrowd—A framework for monitoring of identifiable crowd,” presented at 12th International Conference on Innovations in Information Technology (IIT), Nov. 28–30, 2016, pp. 1–7, doi: https:// doi.org/10.1109/INNOVATIONS.2016.7880036
I. Mahmood, M. Haris, and H. Sarjoughian, “Analyzing emergency evacuation strategies for mass gatherings using crowd simulation and analysis framework: Hajj scenario,” in Proc. 2017 ACM SIGSIM Conf. Princip. Adv. Discret. Simulat., 2017, pp. 231–240, doi: https://doi.org/10. 1145/3064911.3064924
T. Weiss, “Implementing position-based real-time simulation of large crowds,” presented at IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), San Diego, CA, USA, Dec. 9–10, 2019, doi: https://doi.org/10.1016 /j.cag.2018.10.008
I. Karamouzas, N. Sohre, R. Narain, and S. J. Guy, “Implicit crowds: Optimization integrator for robust crowd simulation,” ACM Transac. Graph., vol. 36, no. 4, pp. 1–13, July 2017, https://doi.org/10.1145/3072959. 3073705
A. Malinowski and P. Czarnul, “Multi-agent large-scale parallel crowd simulation with nvram-based distributed cache,” J Computat. Sci., vol. 33, pp. 83–94, 2019, doi: https://doi.org/10.1016/j.procs.2017.05.036
A. Malinowski, P. Czarnul, K. Czuryo, M. Maciejewski, and P. Skowron, “Multi-agent large-scale parallel crowd simulation,” Proc. Comput. Sci., vol. 108, pp. 917–926, 2017, doi: https://doi.org/10.1016/j.procs.2017.05.036
H. M. Al-Ahmadi, I. Reza, A. Jamal, W. S. Alhalabi, and K. J. Assi, “Preparedness for mass gatherings: A simulation-based framework for flow control and management using crowd monitoring data,” Arab. J. Sci. Eng., vol. 46, Jan. 2021, pp. 4985–4997, doi: https://doi.org/10.1007/s13369-020-05322-8
M. V. Ramesh, A. Shanmughan, and R. Prabha, “Context aware ad hoc network for mitigation of crowd disasters,” Ad. Hoc. Net., vol. 18, pp. 55–70, July. 2014, doi: https://doi.org/ 10.1016/j.adhoc.2013.02.006
J. Ondˇrej, J. Pettre, A.-H. Olivier, and S. Donikian, “A synthetic-vision based steering approach for crowd simulation,” ACM Transac. Graph., vol. 29, no. 4, pp. 1–9, July 2010, doi: https://doi.org/10.1145/1778765.1778860
S. Patil, J. Van Den Berg, S. Curtis, M. C. Lin, and D. Manocha, “Directing crowd simulations using navigation fields,” IEEE Transact. Visualiz. Comput. Graph., vol. 17, no. 2, pp. 244–254, 2010, https://doi.org/10. 1109/TVCG.2010.33
M. N. Zali, A. M. Ibrahim, I. Venkat, S. Omar, and B. Belaton, “Crowd management: Simulation modelling and visualization via fuzzy agents,” in AIP Conf. Proc., 2023, doi: https://doi.org/10.1063/5.0110450
A. M. Al-Shaery, M. O. Khozium, N. S. Farooqi, S. S. Alshehri, and M. A. M. B. Al-Kawa, “Problem Solving in Crowd Management Using Heuristic Approach,” IEEE Access, vol. 10, pp. 25422–25434, Mar. 2022, doi: https://doi.org/10.1109/ACCESS.2022.3156008
A. Tufail, “Pilgrim tracking and location based services using RFID and wireless sensor networks,” Int. J. Comput. Sci. Netw. Secur., vol. 18, no. 6, pp. 112–119, June 2018.
M. Martin and P. Nurmi, “A generic large scale simulator for ubiquitous computing,” presented at 3rd Third Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services, San Jose, CA, USA, 2006, pp. 1–3, doi: https://doi.org/10.1109/MOBIQ.2006.340388
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