RAPID: Collaborative Research: Multifaceted Data Collection on the Aftermath of the March 26, 2024 Francis Scott Key Bridge Collapse in the DC-Maryland-Virginia Area
RAPID:协作研究:2024 年 3 月 26 日 DC-马里兰-弗吉尼亚地区 Francis Scott Key 大桥倒塌事故后果的多方面数据收集
基本信息
- 批准号:2427231
- 负责人:
- 金额:$ 8.25万
- 依托单位:
- 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:
- 关键词:
项目摘要
This Rapid Response Research (RAPID) grant project supports research dedicated to a comprehensive and in-depth collection of data to analyze the extensive societal consequences following the Francis Scott Key Bridge collapse in Baltimore, Maryland. This bridge, being a part of the I-695 beltway, lacks convenient detour options. Thus, its collapse leads widespread disruptions in mobility that affected not just the immediate vicinity but also resonated throughout the broader DC-Maryland-Virginia region. The objective of this project is to methodically gather time-sensitive data on traffic flow and community responses in the wake of this event, providing a detailed assessment of its repercussions. Moreover, this incident also brings a major disruption to freight transportation and supply chains on the East Coast. Given the reliance of freight models on occasionally collected, often proprietary data from commodities surveys, state reports, and customs statistics, this study aims to fill these gaps through an integrated approach for critical trucking, maritime, rail, and supply-chain data collection. These efforts are essential for enhancing the resilience of transportation networks and supply chains against future disruptions. For a broader audience, all the collected data will be made publicly available while carefully following rules for privacy protection and existing data usage agreements. Through sharing detailed findings and facilitating a broader understanding of the incident’s impacts, this project aspires to foster a more informed and prepared society, capable of effectively navigating the challenges posed by major infrastructural failures and their far-reaching impacts on communities and economies.The catastrophic collapse of the Francis Scott Key Bridge in Baltimore, Maryland, has precipitated significant disruptions across urban transportation networks, due to the lack of convenient alternative routes, affecting daily commutes for an estimated 34,000 individuals. Moreover, the collapse introduced considerable logistical difficulties, particularly in the Port of Baltimore, a critical national and international trade node. Research completed for this project aims to develop a comprehensive data collection methodology, incorporating data integration and enhancement through existing data platforms (e.g., augmentations in commuting, trucking, rail, and marine traffic data alongside social media analytics), comprehensive surveys (e.g., examining travel behavior, community impact, and economic repercussions), and targeted interviews (e.g., exploring governmental responses and adaptations within the logistics network). Should initial analyses indicate a necessity, the spatial and temporal scope of data collection may be expanded to evaluate the impact of the bridge collapse comprehensively. This project is dedicated to improving data transparency and utility, employing elaborate documentation and a diversified strategy for data dissemination, including the development of a dedicated project website, utilization of the NSF NHERI Data Depot for data storage and dissemination, conducting workshops to engage a wide array of stakeholders, and presenting the data architecture at major transportation, infrastructure systems, and disaster conferences.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.
这个快速反应研究(RAPID)资助项目支持致力于全面深入收集数据的研究,以分析马里兰州巴尔的摩弗朗西斯斯科特关键大桥倒塌后的广泛社会后果。这座桥是I-695环城公路的一部分,缺乏方便的绕行选择。因此,它的崩溃导致了广泛的流动性中断,不仅影响了附近地区,而且在整个哥伦比亚特区-马里兰州-弗吉尼亚州地区引起了共鸣。该项目的目标是有系统地收集关于这一事件发生后交通流量和社区反应的时间敏感数据,详细评估其影响。此外,这一事件也给东海岸的货运和供应链带来了重大中断。鉴于货运模型依赖于偶尔收集的,通常是来自商品调查,国家报告和海关统计数据的专有数据,本研究旨在通过关键的卡车运输,海运,铁路和供应链数据收集的综合方法来填补这些空白。这些努力对于加强运输网络和供应链抵御未来中断的能力至关重要。对于更广泛的受众,所有收集的数据都将公开,同时严格遵守隐私保护规则和现有的数据使用协议。通过分享详细的调查结果和促进更广泛地了解事故的影响,该项目希望培养一个更知情和更有准备的社会,能够有效地应对重大基础设施故障带来的挑战及其对社区和经济的深远影响。由于缺乏方便的替代路线,城市交通网络受到严重干扰,影响了约34 000人的日常通勤。此外,崩溃带来了相当大的物流困难,特别是在巴尔的摩港,一个重要的国家和国际贸易节点。为这个项目完成的研究旨在制定一个全面的数据收集方法,通过现有的数据平台(例如,通勤、卡车运输、铁路和海上交通数据以及社交媒体分析的增强),全面调查(例如,检查旅行行为、社区影响和经济影响),以及有针对性的采访(例如,探讨物流网络内的政府对策和适应措施)。如果初步分析表明有必要,可以扩大数据收集的空间和时间范围,以全面评估桥梁倒塌的影响。该项目致力于提高数据透明度和效用,采用详细的文档和多样化的数据传播策略,包括开发专用项目网站,利用NSF NHERI数据库进行数据存储和传播,举办研讨会以吸引广泛的利益相关者,并在主要交通,基础设施系统,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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Xianfeng Yang其他文献
Surface characterization of silicon nitride powder and electrokinetic behavior of its aqueous suspension
氮化硅粉末的表面表征及其水悬浮液的动电行为
- DOI:
10.1016/j.ceramint.2019.12.215 - 发表时间:
2020 - 期刊:
- 影响因子:5.2
- 作者:
Zhentao Ni;Jie Jiang;Xianfeng Yang;Xiaole Yang;Zhe Zhou;Qinglong He - 通讯作者:
Qinglong He
Theoretical analysis and multi-objective optimization for gradient engineering material arresting system.
梯度工程材料拦阻系统理论分析与多目标优化
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:1.9
- 作者:
Xianfeng Yang;Yun Xing;Zhiqiang Zhang;Jialing Yang;Yuxin Sun - 通讯作者:
Yuxin Sun
Issues for Event Monitoring in Event-Driven Wireless Sensor Networks
事件驱动的无线传感器网络中的事件监控问题
- DOI:
10.1109/wicom.2011.6040345 - 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Lulu Liang;Xianfeng Yang;Linjuan Zhang;D. Gao;Hongke Zhang - 通讯作者:
Hongke Zhang
Efficient genome editing of rubber tree (Hevea brasiliensis) using CRISPR/Cas9 ribonucleoproteins
使用 CRISPR/Cas9 核糖核蛋白对橡胶树(Hevea brasiliensis)进行高效基因组编辑
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:5.9
- 作者:
Yueting Fan;Shichao Xin;Xuemei Dai;Xianfeng Yang;Huasun Huang;Yuwei Hua - 通讯作者:
Yuwei Hua
Crushing behavior and energy absorption of a bio-inspired bi-directional corrugated lattice under quasi-static compression load
仿生双向波纹网格在准静态压缩载荷下的破碎行为和能量吸收
- DOI:
10.1016/j.compstruct.2022.115315 - 发表时间:
2022-01 - 期刊:
- 影响因子:6.3
- 作者:
Bo Li;Hua Liu;Qiao Zhang;Xianfeng Yang;Jialing Yang - 通讯作者:
Jialing Yang
Xianfeng Yang的其他文献
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{{ truncateString('Xianfeng Yang', 18)}}的其他基金
Collaborative Research: OAC Core: Stochastic Simulation Platform for Assessing Safety Performance of Autonomous Vehicles in Winter Seasons
合作研究:OAC Core:用于评估冬季自动驾驶汽车安全性能的随机仿真平台
- 批准号:
2234292 - 财政年份:2022
- 资助金额:
$ 8.25万 - 项目类别:
Standard Grant
CAREER: Physics Regularized Machine Learning Theory: Modeling Stochastic Traffic Flow Patterns for Smart Mobility Systems
职业:物理正则化机器学习理论:为智能移动系统建模随机交通流模式
- 批准号:
2234289 - 财政年份:2022
- 资助金额:
$ 8.25万 - 项目类别:
Standard Grant
Collaborative Research: OAC Core: Stochastic Simulation Platform for Assessing Safety Performance of Autonomous Vehicles in Winter Seasons
合作研究:OAC Core:用于评估冬季自动驾驶汽车安全性能的随机仿真平台
- 批准号:
2106991 - 财政年份:2021
- 资助金额:
$ 8.25万 - 项目类别:
Standard Grant
CAREER: Physics Regularized Machine Learning Theory: Modeling Stochastic Traffic Flow Patterns for Smart Mobility Systems
职业:物理正则化机器学习理论:为智能移动系统建模随机交通流模式
- 批准号:
2047268 - 财政年份:2021
- 资助金额:
$ 8.25万 - 项目类别:
Standard Grant
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