Urban Roadway in America: The Amount, Extent, and Value
Bibliographic record
Abstract
We predicted the amount, share, and value of land dedicated to roadways within and across 316 U.S. primary metropolitan statistical areas. Despite the amount and value of land dedicated to roadways, our study provides the first such estimate across a broad range of metropolitan areas. Our basic approach was to estimate roadway widths using a 10% sample of widths provided by the Highway Performance Monitoring System and apply our estimates to the rest of the roadway system. Multiplying estimated widths by segment length and netting out double counting at intersections provided estimates of land area. We also matched roadway segments and areas to existing land value estimates and satellite-based measures of urbanized land. We found that a little less than a quarter of urbanized land—roughly the size of West Virginia—was dedicated to roadway. This land was worth around $4.1 trillion in 2016 and had an annualized value that was higher than the total variable costs of the trucking sector and the total annual federal, state, and local expenditures on roadways. Conducting a back-of-the-envelope cost–benefit analysis, we found that the country likely has too much land dedicated to urban roads. Federal, state, and local agencies dedicate substantial time, money, and resources to providing roadways. Even with relatively generous assumptions and no external costs from driving, however, we estimated that the average cost of expanding roadways exceeded the benefits by a factor of nearly three when accounting for land value. Policymakers should question policies focused on roadway expansion and consider options to reduce the amount of space dedicated to roadway in favor of more housing, offices, and other land uses. In addition to our findings, we provide a novel data set that academics and policymakers can use to draw their own conclusions about the state of America’s urban roadways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".