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Record W4231601299 · doi:10.1177/0361198105192400111

Gender-Based Analysis of Work Trip Mode Choice of Commuters in Suburban Montreal, Canada, with Stated Preference Data

2005· article· en· W4231601299 on OpenAlexaffabout
Zachary Patterson, Gordon O. Ewing, Murtaza Haider

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcGill University
Fundersnot available
KeywordsPreferenceTravel behaviorMode choiceRevealed preferenceWork (physics)Affect (linguistics)Public transportSurvey data collectionDemand managementDemographic economicsEconomicsPsychologyTransport engineeringEngineeringEconometrics

Abstract

fetched live from OpenAlex

Transportation literature suggests that men and women differ in their commuting patterns and in their propensity to switch between travel options. In North America, it is expected that women will have an increasing impact on travel demand. As such, differences in female responses to travel demand management strategies are likely to become increasingly important as governments try to curtail travel demand. This paper uses a 1994 stated preference survey of suburban commuters in Montreal, Canada, to determine whether there is evidence for differences between men and women in the factors that affect work trip choices, to quantify those differences, and to suggest what the differences imply for travel demand management in the future in Montreal. The main conclusions of this paper are as follows. First, women and men should be modeled separately for work trip mode choice. Second, three main differences appear from the econometric models: women are less likely to choose public transit than men; women are more likely to choose to rideshare; and women are less time-sensitive in regard to commuting than men are.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.053
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.008
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.227
GPT teacher head0.425
Teacher spread0.198 · 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.

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

Citations42
Published2005
Admission routes2
Has abstractyes

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