Pareto Improvements from Lexus Lanes: The effects of pricing a portion of the lanes on congested highways
Bibliographic record
Abstract
Abstract. This paper shows that a judiciously designed toll applied to a portion of the lanes of a highway can be a Pareto improvement even before the revenue is spent. I achieve this new result by extending a standard dynamic congestion model to reflect an important additional traffic externality which transportation engineers have recently identified: additional traffic does not simply increase travel times, but can also introduce additional frictions that reduce throughput. By using a time varying toll to smooth the rate that people depart for work it is possible to avoid these frictions, increasing speed and throughput. Increasing throughput shortens rush hour, which directly helps all road users. However, adding tolls changes the currency used to pay for use of the highway during rush hour from time to money. This change hurts the inflexible poor and most of the time will outweigh the benefit they reap from having a shorter rush hour. We can avoid hurting the inflexible poor by only adding tolls to a portion of the lanes. Doing so preserves their ability to pay with time instead of money. When there are two families of agents, one rich and the other poor, then as long as some rich drivers use the highway at the peak of rush hour then adding tolls to a portion of the lanes is a Pareto improvement. To confirm the real world relevance of this theoretical possibility I use survey and travel time data to estimate the effects of adding optimal time varying tolls. I find that adding tolls to a fourth of the lanes is a Pareto improvement, and that social welfare gains of doing so are over a thousand dollars per road user per year. 1.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".