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.

这项快速反应研究（RAPID）奖将收集交通流模式，过境乘客，个人旅行轨迹和旅行行为的时间敏感数据，这些数据来自宾夕法尼亚州费城I-95州际公路大桥最近的坍塌。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，特别是公交的使用。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一潜力至关重要。然而，这种全面的数据还没有收集与以前的公路基础设施故障。由于桥梁修复预计将在几个月内完成，费城的交通模式已经开始改变，因此迫切需要在重要信息丢失之前启动数据收集工作。该项目旨在通过多尺度和多利益攸关方数据收集方法填补这一关键数据缺口。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最近在费城的高速公路桥梁倒塌和交通使用，数据有可能得出关于人为或自然灾害导致的被迫旅行行为过程的普遍见解。该项目涉及多个规模的数据收集，通过与各种利益相关者建立现有的合作伙伴关系，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交通管理局（SEPTA）合作收集的路线级过境乘客数据;（iii）使用Gravy Analytics收集的来自移动的设备的GPS数据的个人水平的旅行轨迹;以及（iv）来自综合旅行行为调查的关于个人旅行行为、态度和社会人口特征的信息。一个包容性和具有成本效益的事件触发抽样方法将克服与数据收集有关的统计挑战。这种方法将确保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，方法是对关键的离散时间点进行采样，在这些时间点上，通过低成本的聚合交通流量测量观察到大量的行为变化。生成的数据集将被传输并存储在德雷克塞尔大学的计算设施中，并与更广泛的研究社区和公众共享。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。