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Record W2296167984 · doi:10.1139/cjce-2015-0336

Myopic choice or rational decision making? An investigation into mode choice preference structures in competitive modal arrangements in a multimodal urban area, the City of Toronto

2016· article· en· W2296167984 on OpenAlexafffundvenueabout
Mohamed S. Mahmoud, Adam Weiss, Khandker Nurul Habib

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModalMode choicePreferenceMode (computer interface)Discrete choiceSample (material)Empirical researchPhenomenonComputer scienceEconomicsTransport engineeringEconometricsOperations researchMicroeconomicsEngineeringMathematicsStatisticsPublic transport

Abstract

fetched live from OpenAlex

This paper presents an investigation into the preference structure of commuting mode choice in dense urban areas. The paper aims to investigate the phenomenon of myopic choice and extends the phenomenon to the concept of modal culture. Using a household travel diary survey from the greater Toronto and Hamilton Area, an empirical discrete choice model was estimated. This model was used to provide general comments on the commuting and dependent behaviour of the sample, with a particular focus on the factors that influence bicycling captivation and culture. The model was then used for a hypothetical policy scenario analysis, which found that an investment in biking infrastructure had the capacity to increase bicycling mode share by nearly 50%. Based on this result, this paper recommends further investigation into both data collection for more comprehensive empirical model development and investigation into the policy applicability of the proposed model structure.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.796
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.029
GPT teacher head0.283
Teacher spread0.255 · 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

Citations8
Published2016
Admission routes4
Has abstractyes

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