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<p>Drayage trucks are a significant emission source in freight-intensive port corridors. Their short-haul and predictable operational patterns make them strong candidates for zero-emission technology adoption. Accurate identification of their powertrain and cargo types is critical for tracking policy implementation, planning infrastructure, and reducing empty-haul emissions. However, conventional methods rely on manually labeled datasets, and few studies address powertrain classification. This study introduces ZeroDray, a zero-shot classification framework that enables a domain-informed vision-language model to identify drayage truck attributes. Domain-informed prompts integrate expert knowledge, visual evidence, and spatial reasoning to generate interpretable predictions with human-readable explanations for transparency and validation. The framework was evaluated on 443 distinct images collected along a highway corridor serving the Ports of Los Angeles and Long Beach and achieved <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({F}_{1}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math></EquationSource> </InlineEquation> scores above 92 percent across 11 powertrain-cargo classes. ZeroDray offers a framework for tracking zero-emission drayage truck adoption and supporting data-driven sustainable freight regulations.</p>

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Domain informed vision language model for sustainable freight with drayage truck powertrain and cargo classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月2日

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  • Guoliang Feng,
  • Yiqiao Li,
  • Andre Y. C. Tok,
  • Stephen G. Ritchie

摘要

Drayage trucks are a significant emission source in freight-intensive port corridors. Their short-haul and predictable operational patterns make them strong candidates for zero-emission technology adoption. Accurate identification of their powertrain and cargo types is critical for tracking policy implementation, planning infrastructure, and reducing empty-haul emissions. However, conventional methods rely on manually labeled datasets, and few studies address powertrain classification. This study introduces ZeroDray, a zero-shot classification framework that enables a domain-informed vision-language model to identify drayage truck attributes. Domain-informed prompts integrate expert knowledge, visual evidence, and spatial reasoning to generate interpretable predictions with human-readable explanations for transparency and validation. The framework was evaluated on 443 distinct images collected along a highway corridor serving the Ports of Los Angeles and Long Beach and achieved \({F}_{1}\) F 1 scores above 92 percent across 11 powertrain-cargo classes. ZeroDray offers a framework for tracking zero-emission drayage truck adoption and supporting data-driven sustainable freight regulations.