This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta-Bernoulli Bayesian learning model is first applied to measure changes of agents' risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness-to-pay for flood insurance, are measured. Using Miami-Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre-Flood Insurance Rate Maps subsidies, risk-based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high-risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management.
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This content will become publicly available on June 24, 2025
iPrism: Characterize and Mitigate Risk by Quantifying Change in Escape Routes
This paper addresses the challenge of ensuring the safety of autonomous vehicles (AVs, also called ego actors) in realworld
scenarios where AVs are constantly interacting with other actors. To address this challenge, we introduce iPrism which
incorporates a new risk metric – the Safety-Threat Indicator (STI). Inspired by how experienced human drivers proactively
mitigate hazardous situations, STI quantifies actor-related risks by measuring the changes in escape routes available to the
ego actor. To actively mitigate the risk quantified by STI and avert accidents, iPrism also incorporates a reinforcement learning (RL) algorithm (referred to as the Safety-hazard Mitigation Controller (SMC)) that learns and implements optimal risk mitigation policies. Our evaluation of the success of the SMC is based on over 4800 NHTSA-based safety-critical scenarios. The results show that (i) STI provides up to 4.9× longer lead-time for-mitigating-accidents compared to widely-used safety and planner-centric metrics, (ii) SMC significantly reduces accidents by 37% to 98% compared to a baseline Learning-by-Cheating (LBC) agent, and (iii) in comparison with available state-of-the-art safety hazard mitigation agents, SMC prevents up to 72.7% of accidents that the selected agents are unable to avoid. All code, model weights, and evaluation scenarios and pipelines used in this paper are available at: https://zenodo.org/doi/10.5281/
zenodo.10279653.
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- Award ID(s):
- 2029049
- PAR ID:
- 10546461
- Editor(s):
- nd
- Publisher / Repository:
- Institute of Electrical and Electronics Engineers
- Date Published:
- Edition / Version:
- 1
- Volume:
- 1
- Issue:
- 1
- ISBN:
- 979-8-3503-4105-8
- Page Range / eLocation ID:
- 142 to 155
- Subject(s) / Keyword(s):
- Autonomous Vehicles Autonomous Driving Safety Risk Assessment Safety-hazard Mitigation
- Format(s):
- Medium: X Size: 962 kb Other: pdf
- Size(s):
- 962 kb
- Location:
- Brisbane, Australia
- Sponsoring Org:
- National Science Foundation
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