Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations
It addresses efficient traffic management for large transportation networks during hurricane evacuations, but is incremental as it combines existing methods.
This paper tackled traffic prediction during hurricane evacuations by integrating MLP and LSTM models to capture long-term congestion and short-term speed patterns, achieving 82% accuracy for congestion states over 6 hours and MAPEs of 7-13% for speed predictions.
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.