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Record W2785877412 · doi:10.1139/cjce-2017-0442

Subway user behaviour when affected by incidents in Toronto (SUBWAIT) survey — A joint revealed preference and stated preference survey with a trip planner tool

2018· article· en· W2785877412 on OpenAlexaffvenueabout
Teddy Lin, Siva Srikukenthiran, Eric J. Miller, Amer Shalaby

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRespondentPreferenceMode choiceRevealed preferenceTRIPS architectureTransport engineeringTransit (satellite)Service (business)Choice setPlannerPublic transportOperations researchComputer scienceLevel of serviceMode (computer interface)EngineeringEconometricsBusinessStatisticsMarketingEconomicsMathematics

Abstract

fetched live from OpenAlex

Transit user behavioural response under disrupted service conditions, specifically how transit riders choose among available mode options to complete their trips, is not well understood. This study aimed to investigate transit user mode choice in response to rapid transit service disruption in the City of Toronto, incorporating such factors as the type of disruption, stage of the passenger’s trip (pre-trip or en-route), weather conditions, and uncertainty of delay duration. A joint revealed preference (RP) and stated preference (SP) survey was designed where the RP part gathered information on the respondent’s actual response to the most recent service disruption while the SP part solicited the respondent’s travel choices under a set of hypothetical service disruption scenarios. A transit trip planner tool was developed to generate alternative transit mode and path options to avoid the disrupted segment. An empirical model using RP data is presented to verify the survey design technique.

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)0.001
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 score0.764
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.190
Teacher spread0.118 · 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

Citations19
Published2018
Admission routes3
Has abstractyes

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