More eyes on the road: Sensing flooded roads by fusing real-time observations from public data sources

Reliable sensing of road conditions during flooding can facilitate safe and efficient emergency response, reduce vehicle-related fatalities, and enhance community resilience. Existing situational awareness tools typically depend on limited data sources or simplified models, rendering them inadequate for sensing dynamically evolving roadway conditions. Consequently, roadway-related incidents are a leading cause of flood fatalities (40%-60%) in many developed countries. While an extensive network of physical sensors could improve situational awareness, they are expensive to operate at scale. This study proposes an alternative-a framework that leverages existing data sources, including physical, social, and visual sensors and physics-based models, to sense road conditions. It uses source-specific data collection and processing, data fusion and augmentation, and network and spatial analyses workflows to infer flood impacts at link and network levels. A limited case study application of the framework in Houston, Texas, indicates that repurposing existing data sources can improve roadway situational awareness. This framework offers a paradigm shift for improving mobility-centric situational awareness using open-source tools, existing data sources, and modern algorithms, thus offering a practical solution for communities. The paper's contributions are timely: it provides an equitable framework to improve situational awareness in an epoch of climate change and exacerbating urban flood risk.