摘要: |
This study develops a retrospective Roadway Flood Severity Index (RFSI) capable of integrating geo-located hydrometeorological data and roadway information across localized and multi-state, sub-national regions in order to (1) categorize larger-scale, flood-related transportation disruptions, (2) understand the origins of those disruptions, and (3) identify severity risk levels of individual road segments and broader regions of transportation disruption during flood events. The fundamental question is, as flooding events unfold, can past hydrometeorological inundation information be coupled with transportation system network and mobility data to identify the most vulnerable roadway segments and regions? To address this question, the following tasks are in progress: (1) An ex-post facto analysis of historical flood events and the disruptions that they caused to multi-state, sub-national transportation mobility, focusing on how distinct flood types impact the road network and if any other compounding characteristics (e.g., regional variations, types of flooding, road-specific information) can be identified; (2) A mathematical, machine-learning based RFSI to highlight road segment flood risk for two multi-state regions; and (3) Development of a minimally-viable visualization tool to disseminate RFSI outputs with an emphasis on promotion of data interoperability. |