  The objective of this study is to predict the near-future flooding status of
road segments based on their own and adjacent road segments current status
through the use of deep learning framework on fine-grained traffic data.
Predictive flood monitoring for situational awareness of road network status
plays a critical role to support crisis response activities such as evaluation
of the loss of access to hospitals and shelters. Existing studies related to
near-future prediction of road network flooding status at road segment level
are missing. Using fine-grained traffic speed data related to road sections,
this study designed and implemented three spatio-temporal graph convolutional
network (STGCN) models to predict road network status during flood events at
the road segment level in the context of the 2017 Hurricane Harvey in Harris
County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering
the adjacency and distance between road segments, while Model 2 contains an
additional elevation block to account for elevation difference between road
segments. Model 3 includes three blocks for considering the adjacency and the
product of distance and elevation difference between road segments. The
analysis tested the STGCN models and evaluated their prediction performance.
Our results indicated that Model 1 and Model 2 have reliable and accurate
performance for predicting road network flooding status in near future (e.g.,
2-4 hours) with model precision and recall values larger than 98% and 96%,
respectively. With reliable road network status predictions in floods, the
proposed model can benefit affected communities to avoid flooded roads and the
emergency management agencies to implement evacuation and relief resource
delivery plans.
