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This research aims to improve the accuracy of traffic state estimation while minimizing training time using hybrid deep transfer learning.

April 10, 2026Open Access

Hybrid Physics-Guided Deep Transfer Learning For Accurate Traffic State Estimation

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Hybrid learning techniques enhance traffic state estimation accuracy, reducing training time in transportation systems.

This research aims to improve the accuracy of traffic state estimation while minimizing training time using hybrid deep transfer learning.
Develop a hybrid physics-regulated deep transfer learning framework.
Introduce two transfer learning variants for feature extraction from pre-trained models.
Eliminate physics-based loss calculations during training to lower computational overhead.
Achieved over 12% improvement in estimation accuracy compared to traditional PRDL methods.
Reduced training time by more than 50% on average compared to conventional approaches.

Kumar et al. (2026) studied this question.

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