SAFER-predictor: Sparse adversarial training framework for robust traffic prediction under missing and noisy data

SAFER-predictor: Sparse adversarial training framework for robust traffic prediction under missing and noisy data

Accurate traffic flow forecasting is essential for developing intelligent transportation systems (ITSs) to reduce congestion, optimize road management, and improve safety. While data-driven traffic prediction approaches have shown high accuracy, they rely heavily on precise measurements, making them...

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Bibliographic Details
Main Authors: Yutian Liu, Chengfeng Jia, Soora Rasouli, Jian Gong, Tao Feng, Melvin Wong, Tianjin Huang
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Communications in Transportation Research
Subjects:
Traffic prediction
Adversarial training (AT)
Graph neural network
Deep learning
Missing data
Prediction robustness
Online Access:http://www.sciencedirect.com/science/article/pii/S2772424725000320
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http://www.sciencedirect.com/science/article/pii/S2772424725000320

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