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.

该快速响应研究补助金 (RAPID) 奖项将支持创建一个集中式交互式在线仪表板的研究，用于收集和整理 2024 年 3 月 26 日弗朗西斯斯科特基大桥倒塌期间和之后巴尔的摩及其周边地区的交通数据。当前的焦点是马里兰州巴尔的摩弗朗西斯斯科特基大桥倒塌的后果，预计调查结果将产生超出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内的出行行为和长期的。这些知识将帮助交通规划者和工程师制定战略和政策，旨在减少未来基础设施故障的影响，加强应急响应协议并培育有弹性的交通网络。该仪表板将为公众和政府机构提供调整其行为所需的关键信息。研究活动将纳入本科生和研究生课程，促进研究与教育的协同作用。此外，为了扩大参与范围，该项目将通过德雷塞尔大学的本科水平研究课程和夏季 STEM 项目吸引学生，积极寻求让本科和研究生阶段代表性不足的少数族裔学生参与进来。该项目将使用多个公开数据课程收集弗朗西斯斯科特基大桥倒塌期间和之后的当地和区域交通数据。将采用多尺度抽样方法来协调大规模数据采集的需要和效率的需求。具体来说，将在巴尔的摩和马里兰州的安妮阿伦德尔县和霍华德县等附近县收集整个道路网的链路级交通数据，而仅收集该州哈福德县等更广泛地区的主要干线和高速公路的数据马里兰州。在该项目的生命周期之外，该数据集将继续使学术界受益，使研究人员能够研究交通基础设施故障的交通适应性。此外，当未来发生类似灾难时，仪表板将成为未来数据收集和管理工作的可重复使用工具，这将通过提供及时的信息来大大缩短个人和政府机构的响应时间。该奖项反映了 NSF 的法定使命，并得到了国家科学基金会的认可。通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。