Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations
<|reference_start|>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 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.<|reference_end|>
@article{jiang2024deploying,
    title={Deploying scalable traffic prediction models for efficient management in
  real-world large transportation networks during hurricane evacuations},
    author={Qinhua Jiang, Brian Yueshuai He, Changju Lee, Jiaqi Ma},
    journal={arXiv preprint arXiv:2406.12119},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12119},
    primaryClass={cs.LG cs.AI cs.SI}
}