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

arXiv: https://arxiv.org/abs/2406.12119 · HTML: https://mlsanity.com/paper/2406.12119/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 64 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [How to Evaluate Uncertainty Estimates in Machine Learning for Regression?](https://mlsanity.com/paper/2106.03395/) (2021)
- [Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network](https://mlsanity.com/paper/2202.10508/) (2022)
- [A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation](https://mlsanity.com/paper/2202.12505/) (2022)
- [A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models](https://mlsanity.com/paper/2307.05946/) (2023)
- [Mitigating Forgetting in Online Continual Learning via Contrasting Semantically Distinct Augmentations](https://mlsanity.com/paper/2211.05347/) (2022)
- [An Attention-Based Multi-Context Convolutional Encoder-Decoder Neural Network for Work Zone Traffic Impact Prediction](https://mlsanity.com/paper/2405.21045/) (2024)

## Built on by (most cited first, 2 of 2)

- [SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization](https://mlsanity.com/paper/2506.03548/) (2025)
- [Reinforcement Learning-Guided Dynamic Multi-Graph Fusion for Evacuation Traffic Prediction](https://mlsanity.com/paper/2601.06664/) (2026)

## Abstract (arXiv)

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.