Investing in California’s Transportation Future: 2022 Public Opinion on Critical Needs
Bibliographic record
Abstract
This study surveyed 3,821 adults living in California about their general travel behaviors and resources, use of ride-hailing, performance ratings for the transportation system and agencies responsible for transportation, transportation system improvement priorities, and preference for how transportation funds are allocated. Key findings include the following: • Californians are multi-modal: Although driving was the most common mode, respondents reported that in the previous 30 days 66% had made a walk trip, 28% had used ridehailing, 25% had used public transit, and 22% had bicycled. • Although many respondents had at least once substituted ride-hailing for transit, walking, or bicycling and micromobility, the impact on those modes was nuanced. For example, although 64% of respondents who used ride-hailing had done so at least once when transit was available, only about a quarter of ride-hailers (27%) felt that they used transit less once they started ride-hailing. Another 16% of ride-hailers said they rode transit more after they started ride-hailing. • Virtually all respondents—over 90%—wanted the state to work towards better safety and maintenance; reduced congestion, greenhouse gas emissions, and air pollution; and convenient multimodal travel options. • Large majorities of respondents placed a medium or high priority on transportation spending options to support all modes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".