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<p>Understanding spatiotemporal patterns of charging infrastructure is critical for a successful transition to zero-emission mobility. However, most existing studies on light-duty vehicle (LDV) charger emphasize demographic correlations or spatial coverage while overlooking alignment with electric vehicle miles traveled (EVMT), and research on medium- and heavy-duty vehicle (MHDV) charger remains limited. This study develops a spatiotemporal framework to evaluate the distributional alignment of LDV and MHDV charger deployment with travel demand in California from 2020 to 2024. Results reveal LDV charger deployment shows a strong correlation with historical gas station locations, with Pearson coefficients increasing from 0.88 to 0.92. The charger-to-EVMT Gini coefficient increased from below 0.10 to 0.17 by 2024, which indicates growing misalignment with electric travel demand. Early MHDV charger deployment exhibits similar patterns and misalignment. The proposed framework provides a transferable approach for identifying infrastructure disparities and supporting more demand-responsive planning toward zero-emission transportation.</p>

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Path dependence and spatial disparities in the shift from gasoline to charging infrastructure

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

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  • Guoliang Feng,
  • Guofeng Su

摘要

Understanding spatiotemporal patterns of charging infrastructure is critical for a successful transition to zero-emission mobility. However, most existing studies on light-duty vehicle (LDV) charger emphasize demographic correlations or spatial coverage while overlooking alignment with electric vehicle miles traveled (EVMT), and research on medium- and heavy-duty vehicle (MHDV) charger remains limited. This study develops a spatiotemporal framework to evaluate the distributional alignment of LDV and MHDV charger deployment with travel demand in California from 2020 to 2024. Results reveal LDV charger deployment shows a strong correlation with historical gas station locations, with Pearson coefficients increasing from 0.88 to 0.92. The charger-to-EVMT Gini coefficient increased from below 0.10 to 0.17 by 2024, which indicates growing misalignment with electric travel demand. Early MHDV charger deployment exhibits similar patterns and misalignment. The proposed framework provides a transferable approach for identifying infrastructure disparities and supporting more demand-responsive planning toward zero-emission transportation.