RAPID: Developing an Interactive Dashboard for Collecting and Curating Traffic Data after the March 26, 2024 Francis Scott Key Bridge Collapse

RAPID：开发交互式仪表板，用于收集和管理 2024 年 3 月 26 日 Francis Scott Key 大桥倒塌后的交通数据

• 批准号: 2426947
• 负责人: Zhiwei Chen
• 金额: $ 5万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2426947&HistoricalAwards=false
• 关键词: RAPID; Developing; Interactive; Dashboard; Collecting

This Grants for Rapid Response Research (RAPID) award will support research that will create a centralized, interactive online dashboard for collecting and curating traffic data in and around Baltimore, during and after the Francis Scott Key Bridge collapse on March 26, 2024. Although the immediate focus is on the aftermath of the Francis Scott Key Bridge collapse in Baltimore, Maryland, the findings are expected to yield insights that extend beyond this specific event, contributing to a broader understanding of how transportation infrastructure disruptions influence travel behaviors in both short and long term. This knowledge will aid transportation planners and engineers in devising strategies and policies aimed at reducing the impacts of future infrastructure failures, enhancing emergency response protocols and fostering resilient transportation networks. The dashboard will offer the public and government agencies critical information that they need to adapt their behaviors. Research activities will be incorporated into undergraduate and graduate courses to promote the synergy of research and education. Further, to broaden participation, this project will actively seek to involve under-represented minority students at both the undergraduate and graduate levels by attracting students through an undergraduate-level research course and a summer STEM program at Drexel University. This project will collect both local and regional traffic data during and after the Francis Scott Key Bridge collapse, using multiple publicly available data courses. A multi-scale sampling method will be used to reconcile the need for large-scale data collection and the demand for efficiency. Specifically, link-level traffic data for the entire road network will be collected in Baltimore and nearby counties like Anne Arundel and Howard in Maryland, while data will only be collected for major arterial routes and highways in broader regions such as Harford County in the State of Maryland. Beyond the life cycle of this project, the dataset will continue to benefit the academic community, allowing researchers to study traffic adaptation to transportation infrastructure failures. Additionally, the dashboard will become a reusable tool for future data collection and curation efforts when similar disasters happen in the future, which would much improve the response time of individuals and government agencies by offering timely information.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

快速反应研究（RAPID）奖的赠款将支持研究，该研究将创建一个集中的交互式在线仪表板，用于收集和管理巴尔的摩及其周边地区的交通数据，在2024年3月26日弗朗西斯斯科特关键桥倒塌期间和之后。虽然当前的重点是马里兰州巴尔的摩的弗朗西斯斯科特基桥倒塌事件的后果，但预计调查结果将产生超越这一特定事件的见解，有助于更广泛地了解交通基础设施中断如何影响短期和长期的旅行行为。这些知识将有助于交通规划人员和工程师制定战略和政策，以减少未来基础设施故障的影响，加强应急响应协议，并促进弹性交通网络。仪表板将为公众和政府机构提供他们需要调整行为的关键信息。研究活动将纳入本科生和研究生课程，以促进研究和教育的协同作用。此外，为了扩大参与，该项目将积极寻求在本科和研究生两个层次吸引学生通过本科水平的研究课程和夏季STEM计划在德雷克塞尔大学参与代表性不足的少数民族学生。该项目将收集当地和区域交通数据期间和之后的弗朗西斯斯科特关键桥倒塌，使用多个公开的数据课程。将采用多尺度抽样方法，以兼顾大规模数据收集的需要和对效率的要求。具体而言，将在巴尔的摩和附近的县（如马里兰州的安妮阿伦德尔和霍华德）收集整个公路网的链路级交通数据，而仅收集更广泛地区（如马里兰州的哈福德县）的主要干线和高速公路的数据。在该项目的生命周期之外，该数据集将继续造福学术界，使研究人员能够研究交通适应交通基础设施故障。此外，仪表板将成为一个可重复使用的工具，用于未来发生类似灾害时的数据收集和管理工作，这将通过提供及时的信息大大改善个人和政府机构的响应时间。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。