Quantify uncertainty and bias in state Vehicle Miles Traveled estimates used for benchmarking

Quantify the uncertainty and potential bias in state-reported Vehicle Miles Traveled estimates used to construct human crash benchmarks by validating geographic-specific traffic sampling methodologies against ground-truth data in Arizona, California, and Texas.

Background

Benchmark crash rates rely on state VMT estimates derived from sampling and modeling, which may introduce uncertainty and bias. The authors note the absence of ground-truth validation to calibrate these estimates, affecting the precision of benchmark comparisons.

Addressing this gap would strengthen the foundations of ADS-to-human crash rate analyses by improving exposure measurement reliability across jurisdictions.