@article{fdi:010085128,
  title = {{E}xploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations},
  author = {{C}hapuis, {K}{\'e}vin and {P}ham, {M}. {D}. and {B}rugi{\`e}re, {A}. and {Z}ucker, {J}ean-{D}aniel and {D}rogoul, {A}lexis and {T}ranouez, {P}. and {D}aud{\'e}, {E}. and {T}aillandier, {P}atrick},
  editor = {},
  language = {{ENG}},
  abstract = {{A}t 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. {O}ne approach increasingly used in crisis management is preventive mass evacuation. {H}owever, to implement and evaluate the effectiveness of such strategy can be complex, especially in large urban areas. {M}odeling 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. {O}ne major limitation with simulation of traffic based on individual mobility models is their capacity to reproduce a context of mixed traffic. {I}n this paper, we propose an agent-based model with the capacity to overcome this limitation. {W}e simulated and compared different spatio-temporal evacuation strategies in the flood-prone landlocked area of the {P}huc {X}a district in {H}anoi. {W}e demonstrate that the interaction between distribution of transport modalities and evacuation strategies greatly impact evacuation outcomes. {M}ore 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. {I}n addition, we simulated improved evacuation outcomes through selected modification of the road network.},
  keywords = {{A}gent-based simulation ; evacuation policy ; risk context ; traffic model ; urban area ; {VIET} {NAM} ; {HANOI}},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {G}eographical {I}nformation {S}cience},
  volume = {[{E}arly access]},
  numero = {},
  pages = {[43 p.]},
  ISSN = {1365-8816},
  year = {2022},
  DOI = {10.1080/13658816.2022.2069774},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085128},
}