The Highway Performance Monitoring System, managed by the Federal Highway Administration, provides data on average annual daily traffic volume across roadways in the United States, but it has limited representation of medium- and heavy-duty vehicle traffic on lower-volume roadways that are not part of the national highway system. This gap limits research and policy analysis on the community impacts of truck traffic, especially concerning air quality and public health. To address this, we use random forest regression to estimate medium- and heavy-duty vehicle traffic volumes on network links where these data are missing. The result is a comprehensive vehicle traffic dataset that covers 85.2% of public roadways in the United States. From these data, we also calculate traffic density values for each census block and vehicle class that can serve as a high-resolution surrogate for traffic-related air pollution exposure in public health studies and policy analysis. Our high-resolution spatial data products are rigorously validated and provide a more complete representation of truck traffic than any existing publicly available dataset. These datasets are valuable for transportation planning, public health research, and policy decisions aimed at understanding and mitigating the effects of truck traffic on communities that are disproportionately exposed to air pollution from vehicle traffic.

翻译：美国联邦公路管理局管理的公路性能监测系统提供了全美道路平均年度日交通流量数据，但其对非国家公路系统的低流量道路上中型和重型车辆交通的表征有限。这一数据缺口限制了对卡车交通社区影响（尤其是空气质量和公共卫生方面）的研究与政策分析。为此，我们采用随机森林回归方法，在数据缺失的路网路段上估计中型和重型车辆的交通流量。最终获得的数据集覆盖美国85.2%的公共道路，形成了全面的车辆交通数据库。基于这些数据，我们进一步计算了各人口普查区块及车辆类别的交通密度值，该指标可作为公共卫生研究与政策分析中交通相关空气污染暴露的高分辨率替代参数。我们的高分辨率空间数据产品经过严格验证，相比现有公开数据集能更完整地反映卡车交通状况。这些数据集对于交通规划、公共卫生研究及政策制定具有重要价值，有助于理解和缓解卡车交通对过度暴露于车辆空气污染的社区所产生的影响。