La tarification routière au Québec - Quelles leçons tirer de l’expérience des précurseurs ?
Bibliographic record
Abstract
Economists have long argued that road pricing improves the efficiency of infrastructure development. However, pricing projects for roads remain scarce, often for lack of political support. Quebec is no exception. After the implementation of tolls on portions of highways 25 and 30 in the early 2010s, the issue has faded out of political concern. This research focuses on the mechanisms through which technological innovation, and more specifically the emergence of global satellite-based navigation systems, contributes to the comeback of road pricing on the political agenda. A case analysis of Quebec is compared to four other cases considered as first movers in road pricing: Singapore, Oregon (USA), Germany and Norway. Interviews with local experts helped determine how the streams of solutions, problems and politics converge to enable implementation of road pricing projects. The first movers’ experience demonstrates that new technologies and increasing traffic problems are factors that contribute to an increasing need for pricing, but do not eliminate political hurdles. This suggests that it is better to plan things far ahead of time and move forward slowly in the hope of one day successfully implementing a road pricing project. Depuis longtemps, les économistes défendent l’idée que la tarification routière améliore l’efficacité de production des infrastructures. Pourtant, les projets de tarification demeurent rares sur les routes, souvent faute d’appui politique. Le Québec ne fait pas exception. Après la mise en vigueur des péages sur des portions des autoroutes 25 et 30 au début des années 2010, le débat sur la tarification s’est quelque peu essoufflé. Cette recherche s’intéresse aux mécanismes par lesquels l’innovation technologique, et plus précisément l’émergence des outils de positionnement par satellite, contribue à remettre les projets de tarification routière à l’ordre du jour. Une analyse du cas du Québec est mise en comparaison avec quatre territoires considérés comme des précurseurs en matière de tarification routière : Singapour, l’Oregon (États-Unis), l’Allemagne et la Norvège. Des entrevues auprès d’experts locaux ont permis d’identifier les mécanismes à travers lesquels les courants des problèmes, des solutions et de la politique arrivent à se coupler afin de permettre la mise en œuvre des projets de tarification routière sur ces territoires. L’expérience des précurseurs montre que les nouvelles technologies et l’amplification des problèmes de circulation sont des facteurs qui contribuent à accroître la nécessité de la tarification, mais qu’ils n’éliminent pas pour autant les obstacles politiques. Ce constat suggère qu’il vaut mieux s’y prendre longtemps d’avance et cheminer doucement pour espérer réussir un jour l’implantation d’un projet de tarification routière.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".