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Record W648191175

From freeway to feeway: congestion pricing policies for B.C.'s Fraser River crossings

2008· dissertation· en· W648191175 on OpenAlexfundaboutno aff
Peter Wightman

Bibliographic record

VenueSummit (Simon Fraser University) · 2008
Typedissertation
Languageen
FieldEngineering
TopicUrban Transport Systems Analysis
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSingapore Area Licensing SchemeCongestion pricingRoad pricingTraffic congestionTransport engineeringGeographyEngineering
DOInot available

Abstract

fetched live from OpenAlex

Greater Vancouver’s Fraser River crossings face sizeable traffic demand that results in heavy congestion. This study develops and assesses road pricing policy alternatives that would reduce the economic externalities of congestion on these crossings. Based on current traffic flows, target traffic flows and assumed price elasticities of demand, a simple model is assembled that estimates requisite congestion charge levels by time of day. Case studies of London, Toronto and Orange County (California) provide insights on the effectiveness of different road pricing schemes and highlight the critical factors to be considered when developing a pricing scheme. Pricing policy alternatives that reflect the uniqueness of Greater Vancouver transportation are then built using the estimated required charges and data from the case studies. These alternatives are assessed against a multi-criteria framework. The study recommends a new congestion pricing policy for the George Massey Tunnel, and the Alex Fraser, Pattullo and Port Mann bridges.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.971

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2008
Admission routes2
Has abstractyes

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