Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations
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.
Cite
@article{arxiv.2406.12119,
title = {Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations},
author = {Qinhua Jiang and Brian Yueshuai He and Changju Lee and Jiaqi Ma},
journal= {arXiv preprint arXiv:2406.12119},
year = {2024}
}
Comments
Submitted to IEEE ITS Magazine and currently under review