Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations

TL;DR

This paper presents a scalable traffic prediction system combining MLP and LSTM models to improve evacuation management during hurricanes, demonstrating high accuracy and adaptability in real-world large transportation networks.

Contribution

It introduces an integrated predictive framework that captures both long-term and short-term traffic patterns during hurricane evacuations, addressing data heterogeneity and event uncertainty.

Findings

Achieved 82% accuracy in long-term congestion prediction over 6 hours.

Short-term speed prediction errors ranged from 7% to 13%.

Demonstrated effective deployment in Louisiana's transportation network.

Abstract

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.