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Record W4244447481 · doi:10.32920/ryerson.14652519.v1

The role of parking pricing and parking availability on travel mode choice

2021· preprint· en· W4244447481 on OpenAlexaffabout
Seyedmohsen Alavi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSmart Parking Systems Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBusinessMode choiceTransport engineeringWillingness to payMode (computer interface)Parking guidance and informationEnvironmental economicsMarketingComputer scienceEconomicsPublic transportEngineeringMicroeconomics

Abstract

fetched live from OpenAlex

This research examines the impact of parking pricing and parking availability on potential travel mode substitution among current drivers, in four case study areas in the Greater Toronto Area (GTA). It serves to evaluate opportunities to decrease private vehicle usage among the GTA’s workforce. More specifically, the objective of this study is to analyze whether and to what extent parking pricing and parking availability alter drivers’ willingness to change their mode of transportation. Results from ordered logit models demonstrated that a driver’s willingness to change their mode of transportation was statistically correlated with parking cost and parking availability. Parking availability also impacted the correlation between parking pricing and drivers' willingness to change their mode of transportation. The results from this MRP suggests that interventions focused on reducing driving for commuting purposes may focus on changing parking pricing, but depending on the availability of parking, the impacts of such policy/ program may be different.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.019
GPT teacher head0.269
Teacher spread0.249 · 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
Published2021
Admission routes2
Has abstractyes

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