What was wrong with the toll highway concessions in the Madrid Metropolitan Area?
Bibliographic record
Abstract
Highway concessions are becoming quite popular all around the world as a means to promote private participation in the management and financing of public infrastructure. The congestion problems caused by the limited capacity of the road infrastructure networks in many metropolitan areas are prompting public authorities to adopt the concession approach to improve highway capacity, while at the same time implementing a congestion pricing approach. This paper describes and assesses the case study of a toll highway concession program, recently implemented in the city of Madrid, to build four radial toll highways intended to reduce congestion and at the same time to raise revenues to fund the new infrastructure. The concessions, which started their operations in 2003 and 2004, have not worked as well as expected. There are several reasons for this. The most important one is that they managed to capture only a small share of the traffic in the corridor. This has caused serious financial problems to the concessionaires, who are now on the verge of bankruptcy. This paper analyses both the reasons behind the government’s adoption of the concession approach, and the reasons why this approach ultimately proved unsuccessful. On the basis of the results of this case study, we offer a set of recommendations for policy makers when considering the use of toll highway concession contracts in metropolitan areas
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".