Park-and-Ride Access Station Choice Model for Cross-Regional Commuting
Bibliographic record
Abstract
The paper presents an investigation of park-and-ride access station choices of cross-regional commuters in the Greater Toronto and Hamilton area (GTHA). Data from a household travel survey conducted in 2006 in the GTHA were used for this empirical investigation. The household travel survey data were supplemented by data from transit service operators on park-and-ride station locations, parking lot capacities, parking costs, surrounding land use, and station amenities. Three groups of park-and-ride users were defined: (a) individuals for whom only local transit Toronto Transit Commission (TTC) subway stations were within reasonable reach, (b) individuals for whom only regional transit (GO Train) stations were within reach, and (c) individuals for whom both GO Train and TTC subway stations were within reach. Different model structures and specifications were tested, and three discrete choice models were estimated. Empirical models revealed that access distance and the relative station direction (toward the workplace) were the primary factors that affected transit station choice for park-and-ride options. However, for station distance and relative station direction, commuters were more sensitive to changes in station access distance than to changes in the relative station direction from their households. In addition, the empirical models revealed that local transit park-and-ride users were less sensitive to access distance than were regional transit park-and-ride users. The results of this investigation could be useful in future transit station design projects to attract more commuters to use park-and-ride facilities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".