Comparison of Second-Best and Third-Best Tolling Schemes on a Road Network
Bibliographic record
Abstract
Much of the road pricing literature has focused on deriving second-best optimal tolls when only parts of a network can be tolled or tolling is constrained in other ways. A drawback of second-best tolling is that it requires extensive information on speed–flow curves and demand elasticities throughout the network. Such information is often not readily available, and errors in estimating key parameters could result in tolls that are nonoptimal or even welfare reducing. The purpose of this paper is to explore a simpler alternative policy, dubbed “no-queue” tolling, whereby time-varying tolls are imposed selectively on a road network with the objective of eliminating queuing on the tolled links. No-queue tolling is an example of third-best pricing because the effects of the tolls on other links are disregarded. To explore the merits of no-queue tolling, a dynamic traffic simulator (METROPOLIS) was used to compute no-queue tolls for individual links and cordon rings on a laboratory network. For comparison, second-best flat and time-varying tolls were also computed on the same sets of links. Two results stood out. First, even without accounting for the likely computational and acceptability advantages of no-queue tolling, it appeared to dominate flat tolling and performed relatively well compared to step tolling. Second, the benefits from no-queue tolling exhibited approximately constant returns with respect to the number of links that are tolled. This suggests that no-queue tolling could fruitfully be selectively implemented now, rather than waiting a decade or more for comprehensive road pricing to become feasible.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".