Driving to Suburban Rail Stations
Bibliographic record
Abstract
Current research focuses on pedestrian access to transit; however, commuter trains in outlying urban regions serve populations in low-density areas where many people drive rather than walk to transit services. The determinants that influence how far people are willing to drive to train stations and the factors that determine boardings at suburban train stations have not been formally studied. This paper models suburban commuter travel demand by use of the 2003 Montreal, Quebec, Canada, origin–destination survey and onboard survey data from the Agence Métropolitaine de Transport to identify characteristics of individual trips and station characteristics that influence the driving distance to commuter rail and demand at stations. The models show that methods for estimating pedestrian access distance and number of boardings per transit stop can easily be transferred to estimating driving access distance and the number of boardings per station in the park-and-ride context. The model for passenger boardings by station can be used for estimating either demand for a planned station or the effect of service interventions (e.g., parking spots) on boardings at existing stations. The paper also shows that these approaches can be a valuable tool to transit planners interested in increasing passenger demand on commuter rail through a better understanding of service characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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".