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<p>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.</p>

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Physics-guided estimation of freight vehicle loading status using digital tachograph data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月9日

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  • Jihoon Tak,
  • Jungyeol Hong,
  • Dongjoo Park

摘要

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.