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Record W4403955213 · doi:10.32604/cmc.2024.058888

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice—A Systematic Review

2024· article· en· W4403955213 on OpenAlexaff
Mujahid Ali

Bibliographic record

VenueComputers, materials & continua/Computers, materials & continua (Print) · 2024
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsTransport Canada
FundersSilesian University of Technology
KeywordsDiscrete choiceMode choiceMode (computer interface)Computer scienceArtificial intelligenceMachine learningEngineeringPublic transportTransport engineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

Forecasting travel demand requires a grasp of individual decision-making behavior. However, transport mode choice (TMC) is determined by personal and contextual factors that vary from person to person. Numerous characteristic... | Find, read and cite all the research you need on Tech Science Press

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)0.011
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0050.007
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.271
Teacher spread0.251 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations11
Published2024
Admission routes1
Has abstractyes

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