  Accurate traffic prediction is vital for effective traffic management during
hurricane evacuation. This paper proposes a predictive modeling system that
integrates Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM) models
to capture both long-term congestion patterns and short-term speed patterns.
Leveraging various input variables, including archived traffic data,
spatial-temporal road network information, and hurricane forecast data, the
framework is designed to address challenges posed by heterogeneous human
behaviors, limited evacuation data, and hurricane event uncertainties. Deployed
in a real-world traffic prediction system in Louisiana, the model achieved an
82% accuracy in predicting long-term congestion states over a 6-hour period
during a 7-day hurricane-impacted duration. The short-term speed prediction
model exhibited Mean Absolute Percentage Errors (MAPEs) ranging from 7% to 13%
across evacuation horizons from 1 to 6 hours. Evaluation results underscore the
model's potential to enhance traffic management during hurricane evacuations,
and real-world deployment highlights its adaptability and scalability in
diverse hurricane scenarios within extensive transportation networks.
