Deploying Scalable Traffic Prediction Models for Efficient Management in Real-World Large Transportation Networks During Hurricane Evacuations

Accurate traffic prediction is vital for effective traffic management during hurricane evacuation. This article proposes a predictive modeling system that integrates multilayer perceptron and long short-term memory 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, USA, the model achieved an 82% accuracy in predicting long-term congestion states over a 6-h period during a seven-day hurricane-impacted duration. The short-term speed prediction model exhibited mean absolute percentage errors ranging from 7% to 13% across evacuation horizons from 1 to 6 h. The 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.