RAPID: Impact of Highway Infrastructure Failures on Transit Usage: The Case of the 11 June 2023 I-95 Bridge Collapse in Philadelphia, Pennsylvania

RAPID：高速公路基础设施故障对交通使用的影响：以 2023 年 6 月 11 日宾夕法尼亚州费城 I-95 大桥倒塌事件为例

• 批准号: 2333548
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333548&HistoricalAwards=false
• 关键词: RAPID; Impact; Highway; Infrastructure; Failures

This Grant for Rapid Response Research (RAPID) award will collect time-sensitive data on traffic flow patterns, transit ridership, individual travel trajectories, and travel behavior stemming from the recent collapse of a northbound I-95 interstate highway bridge in Philadelphia, Pennsylvania. In addition to the disruptive nature of the I-95 bridge collapse on daily lives, it also has the potential to bring about individual behavioral changes that promote sustainable transportation, particularly transit, usage. High-resolution data before the bridge collapse, during the bridge reconstruction process, and after the bridge is fully repaired is essential for understanding and harnessing this potential. However, such comprehensive data has not been collected in assocation with previous highway infrastructure failures. As the bridge repair is expected to be completed within months and traffic patterns in Philadelphia have already started to change, it is urgent to initiate data collection efforts before important information is lost. This project aims to fill this critical data gap through a multi-scale and multi-stakeholder data collection approach. The generated datasets will be a valuable resource for the research community and others to investigate the impact of highway infrastructure failures on transit usage. While the focus of this project is on a recent highway bridge collapse and transit use in Philadelphia, the data has the potential to derive generalizable insights about forced travel behavioral processes resulting from man-made or natural disasters.This project involves multiple scales of data collection by working with various stakeholders building on existing partnerships, including: (i) link-level traffic flow data from roads throughout the impacted region of the city collected via collaborations with the Philadelphia Streets Department; (ii) route-level transit ridership data collected via collaborations with the Southeastern Pennsylvania Transportation Authority (SEPTA); (iii) individual-level travel trajectories using GPS data from mobile devices collected using Gravy Analytics; and (iv) information on individual travel behavior, attitudes, and sociodemographic characteristics from a comprehensive travel behavior survey. An inclusive and cost-effective event-triggered sampling approach will overcome statistical challenges related to data collection. This approach will ensure that data are collected from all types of communities and population groups throughout the bridge reconstruction process by sampling critical discrete time points where substantial behavioral changes are observed from low-cost, aggregated traffic flow measures. The generated datasets will be transferred to and stored at computing facilities at Drexel University and shared with the broader research community and the public.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.

这项快速响应研究资助 (RAPID) 奖项将收集有关交通流量模式、公交客流量、个人出行轨迹以及宾夕法尼亚州费城 I-95 州际公路北向大桥最近倒塌事故所产生的出行行为的时间敏感数据。 I-95 大桥倒塌除了对日常生活造成破坏外，还有可能带来个人行为变化，促进可持续交通，特别是公共交通的使用。桥梁倒塌前、桥梁重建过程中以及桥梁完全修复后的高分辨率数据对于了解和利用这种潜力至关重要。然而，尚未收集与以前的高速公路基础设施故障相关的此类全面数据。由于桥梁修复预计将在几个月内完成，并且费城的交通模式已经开始发生变化，因此迫切需要在重要信息丢失之前启动数据收集工作。该项目旨在通过多规模和多利益相关者的数据收集方法来填补这一关键数据空白。生成的数据集将成为研究界和其他人调查高速公路基础设施故障对交通使用的影响的宝贵资源。虽然该项目的重点是费城最近发生的高速公路桥梁倒塌和交通使用情况，但这些数据有可能获得有关人为或自然灾害造成的被迫出行行为过程的普遍见解。该项目涉及多种规模的数据收集在现有伙伴关系的基础上与各利益相关方合作，包括：(i) 通过与费城街道部门合作收集的整个城市受影响地区道路的链路级交通流量数据； (ii) 通过与宾夕法尼亚州东南部交通管理局 (SEPTA) 合作收集的路线级公交客流量数据； (iii) 使用 Gravy Analytics 收集的移动设备 GPS 数据得出个人旅行轨迹； (iv) 通过综合旅行行为调查获得有关个人旅行行为、态度和社会人口特征的信息。具有包容性且具有成本效益的事件触发抽样方法将克服与数据收集相关的统计挑战。这种方法将确保通过对关键的离散时间点进行采样，在整个桥梁重建过程中从所有类型的社区和人群中收集数据，在这些时间点上，通过低成本、聚合的交通流量测量观察到了显着的行为变化。生成的数据集将传输并存储在德雷克塞尔大学的计算设施中，并与更广泛的研究界和公众共享。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。