Utilize Crowd-Sourced Data and Machine Learning Technology to Enhance Planning for Transportation Resilience to Flooding
| 项目名称： | Utilize Crowd-Sourced Data and Machine Learning Technology to Enhance Planning for Transportation Resilience to Flooding |
| 摘要： | Transportation plays a critical role in building community resilience to disasters. The latest federal transportation legislation requires transportation agencies to incorporate resiliency into their transportation planning process. However, agencies like metropolitan planning organizations (MPOs) and emergency management authorities are short of effective tools to assess real-time disaster conditions and affected areas in order to make quick responses. This project aims at developing a decision support system (DSS) that combines non-traditional, crowdsourced big-data with traditional data (e.g. remotely sensed data, geographic information services (GIS), and statistical data) to improve flood risk assessment and enhance transportation readiness for quick response decisions on disaster management. The project focuses on urban flooding. While not all urban flooding is severe enough to threaten lives and property loss, it is the small scale flooding events that reveal the vulnerable sites, segments, and sectors where major damages likely occur when severe storms and hurricanes hit. |
| 状态： | Active |
| 资金： | 87500 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Office of the Assistant Secretary for Research and Technology |
| 项目负责人： | Stearns, Amy |
| 执行机构： | Cooperative Mobility for Competitive Megaregions (CM2) |
| 主要研究人员： | Pan, Qisheng |
| 开始时间： | 20180901 |
| 预计完成日期： | 20190831 |
| 实际结束时间： | 0 |
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