Infrastructure Resilience for Climate Adaptation
Abstract
Developing and maintaining resilient transportation infrastructure is a key strategy for meeting several UN sustainable development goals in the face of climate change-driven extreme flooding events. We present a framework for performing data-driven vulnerability analysis for flooding on existing transportation networks, and use this analysis to inform decision-making about investments for climate adaptation. We apply this approach to study the potential impacts of severe flooding on regional mobility in Senegal, using a combination of flood hazard maps and a travel demand model based on call detail record data. We use the estimated number of infeasible trips as a direct measure of flooding-induced mobility impacts, as well as an objective for minimizing these impacts. We then compare three alternative road network upgrade strategies to assess the extent to which each strategy would preserve network functionality under a given flooding scenario. We illustrate that strategies driven solely by travel demand can lead to underinvestment in roads that are at risk of flooding, while solely focusing on repairing flooded road segments neglects the criticality of those repairs to mobility. For example, in a 100 year flooding scenario with a fixed budget, our strategy that considers both flooding and mobility data can achieve a 53% reduction in the number of infeasible trips, while a strategy that just considers flooding data achieves only a 38% reduction for the same cost. Our framework can be applied more broadly to integrate information from a variety of sources about climate hazards and potential human impacts to make better informed decisions about investments in critical infrastructure systems.
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It is possible to incorporate flood hazard and traffic information in order to uncover vulnerable stretches of road.
“In road networks, it is possible to incorporate flood hazard and traffic information in order to uncover vulnerable stretches of road, especially those with few alternative routes [314].”
From Tackling climate change with machine learning (2023) · cites this paper as [314]
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References 17 · Cited by 6
- Authors
- Gupta, A., Robinson, C., & Dilkina, B.
- Affiliations
- Georgia Institute of Technology (US)
- University of Southern California (US)
- Venue
- Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies
- Publisher
- ACM
- Published
- 2018-06-20
- Pages
- 1-8
- OpenAlex
- W2809134026
- Cited by
- 6
- References
- 17
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 2 · 2020: 1 · 2019: 1
- FWCI
- 1.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Resilience and Vulnerability Analysis
- Physical Sciences › Environmental Science › Global and Planetary Change › Flood Risk Assessment and Management
- Social Sciences › Sociology and Political Science › Disaster Management and Resilience
- Keywords
- infrastructure resilience, climate change adaptation, transport infrastructure, flood vulnerability, extreme floods, regional mobility, travel demand modeling, call detail records, flood hazard mapping, Senegal
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