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Record W2359224595

Smart Commute Workplace Program Business Case Review

2016· article· en· W2359224595 on OpenAlexaboutno aff
Meaghan Mendonca, Jake Schabas, Kyle Kellam, Patrick Forestell

Bibliographic record

VenueTransportation Research Board 95th Annual MeetingTransportation Research Board · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTRIPS architectureLiberian dollarBusinessTransport engineeringBusiness caseTraffic congestionFinanceInvestment (military)Operations managementEngineering
DOInot available

Abstract

fetched live from OpenAlex

This report summarizes an in-depth business case review for the Smart Commute Workplace Transportation Demand Management (TDM) program of Metrolinx in the Greater Toronto and Hamilton Area (GTHA). The study was conducted to determine the value delivered by the program’s investment across the network and impact on regional congestion. The report outlines the workplace program’s operations, strategic alignment, financial costs and select economic impacts, including regional congestion reduction and health benefits. The results estimated a 6:1 benefit cost ratio (BCR) for every dollar invested, along with a corresponding increase in carpooling and active transportation uptake. The study shows that the Smart Commute workplace program has been responsible for a 2% reduction in drive-alone trips across the GTHA Smart Commute network, with some workplaces shifting up to 35% of drive-alone trips to other modes—the equivalent of approximately 40 million fewer km travelled each year, and about five thousand cars taken off the road each day. The document was reviewed by the 13 Smart Commute Transportation Management Associations (TMAs), municipal stakeholders and academics at the University of Waterloo. Key inputs include the extensive survey data collected at the employee level for each Smart Commute member workplace, and Transportation Tomorrow Survey data.

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)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.007
Science and technology studies0.0040.002
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.432
Teacher spread0.349 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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