Assessing the Impact of Hurricanes on Roadway Closures and Accessibility: A Machine Learning-Based Case Study of Hurricanes Ian and Idalia in Florida
SSRN, ISSN: 1556-5068
2024
- 165Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Natural disasters like hurricanes can significantly impact transportation systems, resulting in roadway closures and limited accessibility, with serious economic and societal repercussions. Hurricanes Ian and Idalia, both Category 4 hurricanes, made landfall in southeast Florida in 2022 and northwest Florida in 2023, respectively. They caused extensive destruction and flooding, forcing numerous roadway closures. This paper proposes a machine learning-based approach to evaluate and analyze their impact on roadways using high-resolution satellite images obtained before and after the hurricanes, as well as demographics and other hurricane-related data. The model was developed based on Hurricane Ian’s impact on southeast Florida and was also applied to Taylor County in northwest Florida hit by Hurricane Idalia. Findings indicate that roadway segments were classified as fully closed, partially closed, and open with an overall accuracy of 89%, and 92% and 85% confidence levels for Hurricane Ian and Idalia, respectively. Hurricane Ian’s impacts were most noticeable in heavily populated coastal regions, suggesting more roadway closures and reduced accessibility. The model can be used by agencies in post-disaster recovery efforts to prioritize areas needing immediate attention, facilitating effective resource distribution for reconstruction and restoration.
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