Abstract. Previous tsunami evacuation simulations have mostly been based on arbitrary assumptions or inputs adapted from non-emergency situations, but a few studies have used empirical behavior data. This study bridges this gap by integrating empirical decision data from surveys on local evacuation expectations and evacuation drills into an agent-based model of evacuation behavior for two Cascadia subduction zone (CSZ) communities that would be inundated within 20–40 min after a CSZ earthquake. The model also considers the impacts of liquefaction and landslides from the earthquake on tsunami evacuation. Furthermore, we integrate the slope-speed component from least-cost distance to build the simulation model that better represents the complex nature of evacuations. The simulation results indicate that milling time and the evacuation participation rate have significant nonlinear impacts on tsunami mortality estimates. When people walk faster than 1 m s−1, evacuation by foot is more effective because it avoids traffic congestion when driving. We also find that evacuation results are more sensitive to walking speed, milling time, evacuation participation, and choosing the closest safe location than to other behavioral variables. Minimum tsunami mortality results from maximizing the evacuation participation rate, minimizing milling time, and choosing the closest safe destination outside of the inundation zone. This study's comparison of the agent-based model and the beat-the-wave (BtW) model finds consistency between the two models' results. By integrating the natural system, built environment, and social system, this interdisciplinary model incorporates substantial aspects of the real world into the multi-hazard agent-based platform. This model provides a unique opportunity for local authorities to prioritize their resources for hazard education, community disaster preparedness, and resilience plans.
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Data-driven Agent-based Models for Optimal Evacuation of Large Metropolitan Areas for Improved Disaster Planning
ABSTRACT
Evacuation plans are designed to move people to safety in case
of a disaster. It mainly consists of two components: routing and
scheduling. Joint optimization of these two components with the
goal of minimizing total evacuation time is a computationally hard
problem, specifically when the problem instance is large. Moreover,
often in disaster situations, there is uncertainty regarding the
passability of roads throughout the evacuation time period. In this
paper, we present a way to model the time-varying risk associated
with roads in disaster situations. We also design a heuristic method
based on the well known Large Neighborhood Search framework
to perform the joint optimization task. We use real-world road network
and population data from Harris County in Houston, Texas
and apply our heuristic to find evacuation routes and schedules for
the area. We show that the proposed method is able to find good
solutions within a reasonable amount of time. We also perform
agent-based simulations of the evacuation using these solutions to
evaluate their quality and efficacy.
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- NSF-PAR ID:
- 10403915
- Date Published:
- Journal Name:
- Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems
- Page Range / eLocation ID:
- 1639-1641
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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