NSF Award
2333548
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.<br/><br/>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.<br/><br/>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.
