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Record W2011748091 · doi:10.3141/2006-04

Success and Challenges in Modernizing Streetcar Systems

2007· article· en· W2011748091 on OpenAlexaffabout
Graham Currie, Amer Shalaby

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversity of Toronto
FundersMonash University
KeywordsTransport engineeringTransit systemLight rail transitPublic transportLight railBusinessQuality (philosophy)TelecommunicationsTransit (satellite)Public administrationEngineeringPolitical science

Abstract

fetched live from OpenAlex

On-street running in mixed traffic has been identified as the least desirable right-of-way for light rail and tram systems. While most cities in the developed world have withdrawn streetcar systems, substantial networks have been retained in Melbourne, Australia, and Toronto, Canada. Although some commentators have seen the retention of these systems as visionary, there are substantial challenges to be faced in addressing conflicts between streetcars and rising road traffic. Poor running speeds, unreliability, safety, and difficulties in providing universal access are significant issues for modern streetcar systems. Experiences are described in regard to planning and operating the Melbourne and Toronto streetcar systems. The types of challenges being faced in providing services are contrasted. Programs to address the challenge of creating modern high-quality transit systems out of streetcars are compared. Finally, success strategies in modernizing streetcar systems are identified.

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)0.009
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.004
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.216
GPT teacher head0.424
Teacher spread0.208 · 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 designNot applicable
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

Citations51
Published2007
Admission routes2
Has abstractyes

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