Assessing Park-and-Ride Use and User Reactions to Parking Management Strategies: A Case Study in Puget Sound, Washington
Bibliographic record
Abstract
This study examines the use of park-and-ride facilities in the Central Puget Sound Region of Seattle, Washington to support possible implementation of new parking policies. Many of these lots are currently operating at or over capacity and resources do not exist to expand the lots to provide additional parking spaces. Instead, parking management strategies are being considered that are designed to increase the number of people who are able to use the parking spaces to access transit. An audit of existing facilities was performed using a new methodology to estimate the person efficiency of these lots (i.e., the average number of people served by each space). The person-efficiency of all lots was nearly one person served by each parking space, which confirmed expectations that most users drove alone to the lot. Furthermore, a user intercept survey was performed to obtain more detailed information about how these facilities are actually being used. This survey also collected user feedback on the proposed parking management strategies. The survey revealed that park and ride users are generally unwilling to pay for parking. However, a quarter of respondents indicated that they would be willing to carpool if carpools could avoid the parking fee; thus, pricing may help to improve person efficiency by encouraging carpooling. The same fraction of users also indicated they would be more willing to carpool if carpools were provided guaranteed “carpool only” spaces. The information obtained from this study can help transit agencies implement more effective policies for parking management at 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".