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Record W791580567

OVERCOMING TRAFFIC CONGESTION. A DISCUSSION OF REDUCTION STRATEGIES AND BEHAVIORAL RESPONSES FROM A NORTH-AMERICAN PERSPECTIVE

2002· article· en· W791580567 on OpenAlexaffabout
Darren M. Scott

Bibliographic record

VenueEuropean journal of transport and infrastructure research · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPerspective (graphical)Traffic congestionBalance (ability)Travel behaviorTransport engineeringCongestion pricingDemand managementPublic economicsEconomicsComputer scienceBusinessOperations researchEngineeringPsychology
DOInot available

Abstract

fetched live from OpenAlex

This paper addresses the issue of why measures designed to ease traffic congestion have met with limited success in North American cities. To this end, specific types of supply and demand strategies (i.e., roadway construction, jobs-housing balance and the compressed workweek) are discussed emphasizing behavioral responses to them. With respect to roadway construction, evidence of induced travel from recent studies undertaken in the United States is presented. With respect to the two remaining strategies, two studies conducted in Canada employing simulation models are described. Finally, activity-based travel demand models are discussed as a promising means for forecasting both anticipated and unanticipated behavioral responses to mitigation measures, thereby enabling policy makers to make informed decisions as to whether the measures merit implementation. (A)

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.348
Teacher spread0.298 · 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 designTheoretical or conceptual
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

Citations10
Published2002
Admission routes2
Has abstractyes

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