MétaCan
Menu
Back to cohort
Record W2107832004 · doi:10.1353/urb.2008.a249793

Prospects for Urban Road Pricing in Canada

2008· article· en· W2107832004 on OpenAlexaboutno aff
Robin Lindsey

Bibliographic record

VenueBrookings-Wharton papers on urban affairs · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyRoad pricingTransport engineeringEngineeringTraffic congestion

Abstract

fetched live from OpenAlex

When it opened to traffic in 1997, Highway 407 in Toronto became the world's first all-electronic, barrier-free toll highway, charging tolls based on vehi- cle type, distance driven, time of day, and day of week. But no comparable highways have been established in Canada since then. Of the 415,600 lane- kilometers of paved public roads in Canada, only 385 kilometers are tolled. 1 Except on Highway 407, the tolls do not vary over time, and no area-based road- pricing scheme exists. Moreover, Highway 407 is privately owned and operated. Road pricing as a tool for internalizing congestion or other traffic-related exter- nalities has therefore not proceeded far in Canada, which lags behind the United States and a number of countries in Europe and Asia with respect to pricing. This paper assesses the prospects for urban road pricing in Canada, focus- ing on the role of road pricing for congestion relief rather than environmental benefits or revenue generation, although revenue generation is given some attention. 2 Because congestion pricing has not been implemented anywhere in Canada and detailed plans have not been developed for any city, the possibilities are relatively wide open. The concept of an optimal implementation path for road pricing has been discussed in the literature, but no general and comprehensive rules have yet been established and it is clear that prescriptions must be tailored to city- specific circumstances and opportunities. Not surprisingly, the case for urban road pricing appears to be strongest in Canada's three largest cities: Toronto,

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.226
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueBrookings-Wharton papers on urban affairsSame topicTransportation Planning and OptimizationFrench-language works237,207