Between Pricing and Investment, What Mobility Policies Would Be Advantageous for Île-de-France?
Bibliographic record
Abstract
This article provides a prospective study of mobility policies for the private car and public transit (PT) modes of transportation in the Paris Ile-de-France region. Different economic instruments are considered: pricing of car traffic or transit service, subsidizing PT, and investment in PT to improve service quality. Policy scenarios are defined and assessed according to multiple criteria: users’ benefits, PT production costs and fare revenues, public subsidies, and environmental damage both local (air pollution) and global (carbon emissions). The social, economic, and environmental impacts are monetized and aggregated in a wellbeing function. While a first set of scenarios are specified directly, two other sets of scenarios are calculated by optimizing the wellbeing function with respect to action variables on the transit mode in the medium or long run. The regional mobility system is modeled in a structural way: concentric subregions, travel demand segmented by geographical and behavioral conditions, environmental impacts based on road and rail traffic, and car mode and transit mode depicted each as a set of technical components involving 1 to 3 structural factors that can make action levers. This model-based methodology allows for trading between different kinds of impacts and identifying performance-oriented policy packages.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".