Estimated roadway segment traffic data by vehicle class for the United States: A machine learning approach

The Highway Performance Monitoring System, managed by the Federal Highway
Administration, provides essential data on average annual daily traffic
across U.S. roadways, but it has limited representation of medium- and
heavy-duty vehicles on non-interstate roads. This gap limits research and
policy analysis on the 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 in
areas with sparse data. This results in a more comprehensive dataset,
which enables the estimation of traffic density at the census block level
as a proxy for traffic-related air pollution exposure. Our high-resolution
spatial data products, rigorously validated, provide a more accurate
representation of truck traffic and its environmental and health impacts.
These datasets are valuable for transportation planning, public health
research, and policy decisions aimed at mitigating the effects of truck
traffic on vulnerable communities exposed to air pollution.