Summary of Deploying Scalable Traffic Prediction Models For Efficient Management in Real-world Large Transportation Networks During Hurricane Evacuations, by Qinhua Jiang et al.
Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations
by Qinhua Jiang, Brian Yueshuai He, Changju Lee, Jiaqi Ma
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary 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. The framework is designed to address challenges posed by heterogeneous human behaviors, limited evacuation data, and hurricane event uncertainties. Leveraging various input variables, including archived traffic data, spatial-temporal road network information, and hurricane forecast data, the system achieves high accuracy in predicting long-term congestion states and short-term speed patterns. The model’s potential to enhance traffic management during hurricane evacuations is underscored by its real-world deployment in a Louisiana-based traffic prediction system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict traffic during hurricanes using machine learning models. It combines two types of models: Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM). This helps the model understand both long-term patterns and short-term changes in traffic speed. The model uses information like past traffic data, road maps, and weather forecasts to make predictions. It’s tested in Louisiana during a hurricane evacuation and is shown to be accurate and useful for managing traffic. |
Keywords
* Artificial intelligence * Lstm * Machine learning
