Climate change and sea level rise have increased the frequency and severity of flooding events in coastal communities. This study quantifies transportation impacts of recurring flooding using crowdsourced traffic and flood incident data. Agency-provided continuous count station traffic volume data at 12 locations is supplemented by crowd-sourced traffic data from location-based apps in Norfolk, Virginia, to assess the impacts of recurrent flooding on traffic flow. A random forest data predictive model utilizing roadway features, traffic flow characteristics, and hydrological data as inputs scales the spatial extent of traffic volume data from 12 to 7736 roadway segments. Modeling results suggest that between January 2017 and August 2018, City of Norfolk reported flood events reduced 24 h citywide vehicle-hours of travel (VHT) by 3%, on average. To examine the temporal and spatial variation of impacts, crowdsourced flood incident reports collected by navigation app Waze between August 2017 and August 2018 were also analyzed. Modeling results at the local scale show that on weekday afternoon and evening periods, flood-impacted areas experience a statistically significant 7% reduction in VHT and 12% reduction in vehiclemiles traveled, on average. These impacts vary across roadway types, with substantial decline in traffic volumes on freeways, while principal arterials experience increased traffic volumes during flood periods. Results suggest that analyzing recurring flooding at the local scale is more prudent as the impact is temporally and spatially heterogeneous. Furthermore, countermeasures to mitigate impacts require a dynamic strategy that can adapt to conditions across various time periods and at specific locations.

Climate change and sea-level rise are increasingly leading to higher and prolonged high tides, which, in combination with the growing intensity of rainfall and storm surges, and insufficient drainage infrastructure, result in frequent recurrent flooding in coastal cities. There is a pressing need to understand the occurrence of roadway flooding incidents in order to enact appropriate mitigation measures. Agency data for roadway flooding events are scarce and resource-intensive to collect. Crowdsourced data can provide a low-cost alternative for mapping roadway flood incidents in real time; however, the reliability is questionable. This research demonstrates a framework for asserting trustworthiness on crowdsourced flood incident data in a case study of Norfolk, Virginia. Publicly available (but spatially limited) flood incident data from the city in combination with different environmental and topographical factors are used to create a logistic regression model to predict the probability of roadway flooding at any location on the roadway network. The prediction accuracy of the model was found to be 90.5%. When applying this model to crowdsourced Waze flood incident data, 71.7% of the reports were predicted to be trustworthy. This study demonstrates the potential for using Waze incident report data for roadway flooding detection, providing a framework for cities to identify trustworthy reports in real time to enable rapid situation assessment and mitigation to reduce incident impact.

<p>Nuisance flooding, which is repetitive flooding caused by both tidal and rainfall-driven events, is increasing in frequency and severity for many coastal communities. As climate change causes sea level rise and more frequent and intense storm events, these nuisance flooding events are producing significant disruptions and impacts to coastal communities. The objective of this study is to improve modeling and decision support activities around nuisance flooding and, in particular, its impact on transportation infrastructure. Our study region and partner in the research is the City of Norfolk, Virginia, USA. Norfolk is home to the largest Navy base in the world, the second busiest port on the United States East Coast, and is the second most populous city in Virginia. It is also one of 100 Rockefeller Resilient Cities in the world, committed to taking progressive aims at combating nuisance flooding. Using real-time observational networks, crowdsourced data, physics-based and machine learning modeling approaches, model predictive control, and economic and social science methods, we are exploring ways to better understand and mitigate the impacts of street-scale flooding. Our research is showing how real-time control of stormwater infrastructure systems can help to improve the resilience of these systems during nuisance flooding events by strategically holding back rainfall runoff and preventing tidally driven stormwater backups. We are also showing physics-based and machine-learning methods can be combined for real-time decision support and how reputation system approaches can be used to measure trust in crowdsourced rainfall datasets. This presentation will provide an overview of these and related activities, each aimed at the common goal of leveraging real-time data from a variety of sources, innovative modeling techniques, and community-driven decision making to improve community resilience to nuisance flooding.</p>