Are transit users loyal? Revelations from a hazard model based on smart card data
Bibliographic record
Abstract
Smart card fare collection systems for public transit produce a huge quantity of data on a daily basis. The ability to follow the use of a single card throughout the months gives the opportunity of measuring the loyalty of the individual to the service. Then, operators can have quantitative knowledge of the loyalty in their network. However, it is also important to know what are the factors that influence the survival of the users. This paper presents the application of a discrete time hazard model to 5 years of data of a medium-size transit authority in Canada. The concept of the hazard model relates to the fact that the probability to continue the use of a smart card by user i, at time t, is conditional to the probability of not cancelling the card before the time period. Hence, its use is appropriate in this case. Results for the regular adult fare show that loyalty is positively influenced by residential density and by the transit share in the area. A younger population will also be retained longer in the system. However, a high unemployment rate has a negative impact on survival. A high share of transit and walk trips is also affecting the loyalty, suggesting that the retention is reduced when there are more mode choices available to the commuter.
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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 | 0.022 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
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".