Evacuation Traffic

Predicting traffic flow during hurricane evacuations is crucial for optimizing resource allocation and minimizing delays. Current research focuses on developing accurate, network-wide traffic prediction models using deep learning techniques, such as Graph Convolutional LSTMs, often incorporating data from various sources including traffic sensors and social media. These models leverage transfer learning to adapt from normal traffic patterns to the unique dynamics of evacuation scenarios, aiming to improve prediction accuracy and provide valuable real-time information for emergency management. The improved forecasting capabilities offer significant potential for enhancing evacuation planning and response strategies.