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.

这项用于快速响应研究（快速）奖励的赠款将收集有关交通流量模式，过境乘客，个人旅行轨迹以及旅行行为的时间敏感数据，这是由于最近在宾夕法尼亚州费城的I-95 I-95 I-95 I-95州际公路桥梁崩溃所引起的。除了I-95桥梁在日常生活中崩溃的破坏性外，它还有可能引起个人行为变化，以促进可持续的运输，尤其是过境，使用。在桥梁倒塌之前，在桥梁重建过程中以及桥梁完全修复后，高分辨率数据对于理解和利用这一潜力至关重要。但是，与以前的公路基础架构失败有关，尚未收集这种综合数据。由于预计桥梁维修将在几个月内完成，并且费城的交通模式已经开始改变，因此在丢失重要信息之前，迫切需要启动数据收集工作。该项目旨在通过多尺度和多方利益相关者数据收集方法来填补这一关键的数据差距。生成的数据集将是研究社区和其他人调查公路基础设施故障对公交使用情况的影响的宝贵资源。虽然该项目的重点是费城最近的高速公路桥梁崩溃和过境使用，但数据有可能获得有关由人造或自然灾害引起的强制旅行行为过程的可推广见解。该项目涉及多个数据收集的数据收集，通过与各个利益相关者在现有的合作伙伴关系中构建的群体，包括：费城街头部； （ii）通过与宾夕法尼亚州东南部运输局（SEPTA）合作收集的路线级别过境乘车数据； （iii）使用使用Gravy Analytics收集的移动设备的GPS数据的个人级别旅行轨迹； （iv）有关个人旅行行为，态度和社会人口统计学特征的信息。包容性且具有成本效益的事件触发的抽样方法将克服与数据收集相关的统计挑战。这种方法将通过对关键离散时间点进行抽样，从而从低成本，汇总的交通流量测量中观察到实质性的行为变化，从而确保从所有类型的社区和人群中收集数据。生成的数据集将被转移到德雷克塞尔大学的计算设施中并存储在公众和公众的计算设施中。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来评估的。