Simulation-Based Study of Fire Suppression Guidelines at Chatuchak Weekend Market Using the GAMA Platform
Main Article Content
Abstract
We developed an integer linear programming model in conjunction with simulation programming on the GAMA platform to plan fire suppression strategies, with a focus on resource allocation, specifically the number of firefighters and fire trucks, in order to minimize potential damage. This study uses the Chatuchak weekend market, located in Chatuchak District, Bangkok, Thailand, as a case study. The results from the model indicate that Zones 25 (Wood Carving, Spa, & Incense) and 28 (Second-hand Clothing, & Shoes) are the most difficult area for firefighters to access. Based on simulations conducted with GAMA, it was determined for example that if a fire starts in Zone 18 (Clothing, Camping Gear, & Leather Goods), two fire trucks, each carrying five firefighters, should be deployed. This approach results in the lowest median damage, affecting 15 shop units. The incident was resolved within 4 hours and 12 minutes on average. Conversely, if a fire starts in Zone 15 (Silverware, & Home Decoration), the optimal response involves deploying seven firefighters equipped with portable extinguishers on foot to be able to extinguish the incident within 1 hour and 47 minutes on average, and minimize damage to a median of 4.5 shop units. For fires in other zones, the simulation can similarly assign resource allocation and response strategies.
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Ban TQ, Duong PL, Son NH, Van DT. Covid-19 disease simulation using GAMA platform. In: International Conference on Computational Intelligence. IEEE; 2020. p. 246-51.
Moreno-Espino M, Reyes-Valdés LM, Benitez RR, López-González A, YáñezMárquez C, Hadfeg-Fernández Y. Intelligent agent-based simulation of fire propagation in multiple environments. Contemporary Mathematics. 2025;6:3033-53.
Bandyopadhyay M, Singh V. A GIS and agent-based model to simulate fire emergency response. In: International Congress on Information and Communication Technology: Proceedings of ICICT 2015. Singapore: Springer; 2016. p. 341-9.
Taillandier P, Vo DA, Amouroux E, Drogoul A. GAMA: bringing GIS and multilevel capabilities to multi-agent simulation. In: European Workshop on MultiAgent Systems; 2010.
Nguyen MH, Ho TV, Richaud JC. Modeling and simulation of fire evacuation in public buildings. Advances in Computer Science: An International Journal. 2015;4(18):1-8.
Lee J, Cha M, Choi B, Kim T. A teambased firefighter training platform using the virtual environment. In: Proceedings of the 9th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry; 2010. p. 299-302.
Daudé E, Chapuis K, Taillandier P, Tranouez P, Caron C, Drogoul A, Gaudou B, Rey-Coyrehourq S, Saval A, Zucker JD. ESCAPE: exploring by simulation cities awareness on population evacuation. Valencia: ISCRAM; 2019.
Taillandier P. Traffic simulation with the GAMA platform. In: International Workshop on Agents in Traffic and Transportation; 2014. p. 8.
Taillandier P, Bourgais M, Drogoul A, Vercouter L. Using parallel computing to improve the scalability of models with BDI agents. In: Social Simulation Conference. Cham: Springer International Publishing; 2017. p. 37-47.
Taillandier P, Grignard A, Marilleau N, Philippon D, Huynh QN, Gaudou B, Drogoul A. Participatory modeling and simulation with the GAMA platform. Journal of Artificial Societies and Social Simulation. 2019;22(2):3.
Taillandier P, Bourgais M, Caillou P, Adam C, Gaudou B. A BDI agent architecture for the GAMA modeling and simulation platform. In: International Workshop on Multi-Agent Systems and AgentBased Simulation. Cham: Springer International Publishing; 2016. p. 3-23.
Macatulad EG, Blanco AC. 3DGISbased multi-agent geosimulation and visualization of building evacuation using GAMA platform. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2014;40:87-91.
de Almeida DS, e Abreu FB, BoavidaPortugal I. Agent-based simulation of non-urgent egress from mass events in open public spaces. Simulation Modelling Practice and Theory. 2024;136:103002.
Taillandier P, Vo DA, Amouroux E, Drogoul A. GAMA: a simulation platform that integrates geographical information data, agent-based modeling and multiscale control. In: International Conference on Principles and Practice of MultiAgent Systems. Berlin: Springer; 2010. p. 242-58.
Widyantoro BA, Santosa PB. Network analysis to determine the optimal route for firefighters in Makassar City. In: IOP Conference Series: Earth and Environmental Science. Bristol: IOP Publishing; 2021. p. 012005.
Taillandier P, Gaudou B, Grignard A, Huynh QN, Marilleau N, Caillou P, Philippon D, Drogoul A. Building, composing and experimenting complex spatial models with the GAMA platform. Geoinformatica. 2019;23(2):299-322.
OpenStreetMap contributors. Planet dump [Internet]. 2015 [cited 2024 May 3]. Available from: https://planet.openstreetmap.org
Ranaweera DM, Hemapala KU, Buddhika AG, Jayasekara P. A shortest path planning algorithm for PSO base firefighting robots. In: Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics. IEEE; 2018. p.
-5.
Hidayatullah AA, Handayani AN, Fuady MJ. Performance analysis of A* algorithm to determine shortest path of fire fighting robot. In: International Conference on Sustainable Information Engineering and Technology. IEEE; 2017. p. 53-6.
Helbing D, Molnar P. Social force model for pedestrian dynamics. Physical Review E. 1995;51(5):4282.
Peacock RD, Averill JD, editors. Pedestrian and evacuation dynamics. New York: Springer Science & Business Media; 2011.
Yun HS, Nam DG, Hwang CH. An experimental study on the fire spread rate and separation distance between facing stores in passage-type traditional markets. Energies. 2020;13(17):4458.