Why Do People Use Transit? Model for Explanation of Personal Attitude Toward Transit Service Quality
Bibliographic record
Abstract
This paper presents a critical investigation of reasons for using transit by the residents of the City of Calgary, Canada. Reasons of using transit are expressed as functions of peoples' perception and attitude towards transit service quality and attributes. A multinomial logit model combined with latent variable models is developed to capture unobserved latent variables in defining perceptions and attitude. Using a transit customer satisfaction survey data, conducted in 2007 by Calgary Transit authority, this approach models the reasons of choosing transit and tests the significance of two individual specific latent variables: perceptions of 'reliability and convenience' and 'ride comfort'. It reveals many behavioral details that have important policy implications. Most importantly, it is found that the people of Calgary value 'reliability and convenience' over 'ride comfort'. As for policy implications of the findings, it is clear that improving connectivity of train service, reducing multimodal transfers, and increasing dedicated right-of-way for transit would effectively increase transit ridership in Calgary. In terms of application of passively collected data source, this paper shows how non-research oriented survey data can be used to unravel many behavioral details and policy relevance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".