How Captive Is the Captive Market Anyway? Reexamination of the Impact of Auto Availability
Bibliographic record
Abstract
The concept of the “captive market” for transit has been prevalent in transportation planning agencies for decades. Indeed, many transit agencies focus considerable effort on distinguishing between their “choice” and “captive” markets. This paper does not intend to undermine the theory that a segment of the market, predominantly lower-income riders, is more constrained in their travel choices. However, the paper does argue that the concept of the “captive market” can be applied in an overly deterministic way when members of 0 auto households are essentially locked out of certain travel modes such as auto driver (and/or single occupancy vehicle mode) and drive to transit (park-and-ride). Data from two recent household trip diaries (2008 and 2011) conducted in metropolitan Vancouver are examined to determine whether such rule-based approaches are appropriate for 0-car households, particularly in light of the rise of auto-sharing companies in Vancouver and surrounding cities. The paper will provide an analysis of two different travel patterns of particular relevance for individuals that fall broadly into the captive market category: the car-availability for travelers not using auto modes will be examined in addition to the mode choice of travelers from 0-car households. A detailed examination will be made of those respondents from 0-car household who also indicated that they were a car driver.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".