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Por favor, use este identificador para citar o enlazar este ítem: https://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/169914
Título : Exploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations
Palabras clave : Agent-based simulation;evacuation policy;risk context;traffic model;urban area
Descripción : At a time when the impacts of climate change and increasing urbanization are making risk management more complex, there is an urgent need for tools to better support risk managers. One approach increasingly used in crisis management is preventive mass evacuation. However, to implement and evaluate the effectiveness of such strategy can be complex, especially in large urban areas. Modeling approaches, and in particular agent-based models, are used to support implementation and to explore a large range of evacuation strategies, which is impossible through drills. One major limitation with simulation of traffic based on individual mobility models is their capacity to reproduce a context of mixed traffic. In this paper, we propose an agent-based model with the capacity to overcome this limitation. We simulated and compared different spatio-temporal evacuation strategies in the flood-prone landlocked area of the Phuc Xa district in Hanoi. We demonstrate that the interaction between distribution of transport modalities and evacuation strategies greatly impact evacuation outcomes. More precisely, we identified staged strategies based on the proximity to exit points that make it possible to reduce time spent on road and overall evacuation time. In addition, we simulated improved evacuation outcomes through selected modification of the road network.
URI : https://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/169914
Otros identificadores : https://www.documentation.ird.fr/hor/fdi:010085128
oai:ird.fr:fdi:010085128
Chapuis Kévin, Pham M. D., Brugière A., Zucker Jean-Daniel, Drogoul Alexis, Tranouez P., Daudé E., Taillandier Patrick. Exploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations. 2022, [Early access], p. [43 p.]
Aparece en las colecciones: Institut de Recherche pour le Développement - IRD - Cosecha

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