Metropolitan area heterogeneity and the impact of road infrastructure improvements on VMT
Bibliographic record
Abstract
Past studies have found that the average elasticity of vehicle miles traveled (VMT) to the stock of highways is close to one. This result is often interpreted to mean that an increase in the stock of highway miles is likely to be accompanied by a commensurate increase in VMT, leaving congestion unaffected. In this study, we explore the heterogeneity in this elasticity due to both observed and unobserved city characteristics. We begin by using a simple model to demonstrate how cities with different initial congestion levels may respond differently to added road capacity. These differences give rise to heterogeneity in the elasticity of VMT to highway capacity. We then conduct an empirical analysis using the instrumental variable quantile regression (IVQR) model to incorporate variation in the elasticity due to the presence of unobserved differences across metropolitan statistical areas (MSAs). The IVQR results imply that expanding road capacities has a greater impact on MSAs with low levels of VMT than on MSAs with high VMT. Estimates of the elasticity using generalized quantile regression (GQR) mirror the IVQR estimates. We further simulate the mechanisms using a spatial equilibrium model with an extensive road network calibrated to the Greater Los Angeles region by treating commuting and shopping trips, mode, and route choices. We find that the elasticity of VMT to capacity—which decreases with initial VMT—in LA is 0.32. One important policy implication is that, when building more roads, the mean or median congestion level across cities remains unchanged, although it can reduce the number of cities experiencing high congestion levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".