3. Producción

Browse

Settings

Subject: search.filters.subject.Tsunami evacuation

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Reinforcement learning-based tsunami evacuation guidance system
    (Elsevier Ltd, 2024-12-01)
    Congestion and crowding are critical issues during indoor and outdoor emergency evacuations. In the 2011 Great East Japan Earthquake and Tsunami, vehicle traffic was one of the causes of the event's casualties. After this, vehicle evacuation in tsunami events is not advised in Japan. Then, pedestrian evacuation is expected to be the primary mode of mobility in emergencies. However, crowding and congestion may affect the evacuation time of individuals and the overall outcome of the process. In addition, narrow streets and a high preference for the shortest routes may worsen the situation. This study aims to find the best evacuation route for a target population, considering less congestion in the road network and increasing the chances of reaching safe areas on time. We propose using reinforcement learning to train an intelligent network of agents, placed at the intersections, in charge of the evacuation process to fully complying evacuee agents. The model rewards decisions that lead to successful evacuation, considering the dynamics of departure times and street congestion throughout the simulation. We demonstrate the applicability of reinforcement learning to guide tsunami evacuation in a simulation and test this against a non-guided case where evacuees move following the shortest paths. Results show that the reinforcement learning model yields better outcomes than evacuations following the shortest paths.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Tsunami evacuation planning for Camana, Peru: insights from agent-based modeling
    (Springer, 2026-06-08)
    Coastal communities within the Ring of Fire, such as Camana, Peru, face significant risks from tsunamis due to their proximity to tectonic plate boundaries. This study employs agent-based modeling (ABM) integrated with reinforcement learning (RL) to assess and optimize tsunami evacuation strategies in the Camana resort area. Simulations focused on pedestrian evacuation under varying population densities, exploring the impacts of infrastructure modifications such as additional pathways and vertical evacuation structures. The results highlight that constructing additional pathways to evacuation zones and incorporating vertical evacuation structures significantly improve survival rates. Six vertical evacuation structures are recommended for a population of approximately 4500, while a larger transient population of 20,300 would require up to 14 structures. Additionally, this study emphasizes the importance of public education and rapid response in reducing evacuation delays, as shorter departure times significantly improve outcomes. By leveraging scientific tools, the proposed strategies offer a practical roadmap for decision-makers to improve tsunami preparedness and resilience in high-risk coastal areas.