Accurately distinguishing between empty and loaded freight vehicle operations is essential for improving logistics efficiency, reducing environmental impacts, and supporting evidence-based freight and infrastructure policies. In Korea, where empty running accounts for over 40% of truck trips, reliable load-status information can enable demand-driven routing, enhance freight origin–destination (OD) estimation, strengthen overloading enforcement, and optimize road maintenance strategies. This study proposes a scalable framework for load-status estimation using Digital Tachograph (DTG) data, a legally mandated system that provides high-frequency records of speed, acceleration, and engine RPM without requiring additional sensors. Physics-informed features—derived from vehicle dynamics, drivetrain behavior, and resistance forces—were constructed and used in a Bayesian Neural Network (BNN) classifier to incorporate both predictive accuracy and uncertainty quantification. Empirical results show that the proposed method achieves an average accuracy of 85.3% when using 9-s averages, exceeding 90% at highway speeds, and approaching full accuracy when temporal aggregation is applied. These findings demonstrate not only the technical feasibility of DTG-based estimation but also its capacity for nationwide, real-time monitoring of freight operations. Beyond model performance, the results highlight the broader policy relevance of this approach. By leveraging existing DTG infrastructure, the method offers a cost-effective and field-deployable solution for enhancing freight system visibility, reducing empty running, improving sustainability, supporting overloading detection, and informing infrastructure management. This positions DTG-based load-status estimation as both a methodological contribution to transportation research and a strategic decision-support tool for policymakers and industry stakeholders.
Tak et al. (Mon,) studied this question.