A Temporal Attention-Based SARIMA-BiLSTM Residual Learning Model Tuned by Grey Wolf Optimizer for Parallel Urban Traffic Forecasting

A Temporal Attention-Based SARIMA-BiLSTM Residual Learning Model Tuned by Grey Wolf Optimizer for Parallel Urban Traffic Forecasting

Accurate and timely traffic forecasting is essential for ensuring the efficiency, reliability, and safety of modern transportation networks. However, urban traffic exhibits high levels of temporal variability, spatial complexity, and nonlinear dependencies, posing challenges to traditional time seri...

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Bibliographic Details
Main Authors: Manas Kamal Das, Christopher Columbus Chinnappan, E. Elakiya
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
BiLSTM
deep learning
Grey Wolf Optimizer
hyperparameter tuning
parallel computing
residual learning
Online Access:https://ieeexplore.ieee.org/document/11083601/
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