Station Area Planning and Parking Management in the Urban Core: Cases in Oakland and Berkeley
Bibliographic record
Abstract
Planning for transit-oriented infill development often calls for adding new buildings, residents, and attractions within a quarter to half-mile radius of the station. This paper discusses planning and parking management research conducted for three station areas of the San Francisco Bay Area Rapid Transit District (BART), all located in older commercial districts. The research is based on interviews with key stakeholders, parking occupancy and turnover studies, counts, and surveys of users of the three station areas. This research provides insights into the complexities of managing parking in areas where off-street parking is limited and on-street parking is regulated in a variety of ways. The stakes are high in city centers and high density urban districts, as different constituent groups have conflicting views and recommended solutions on whether and how parking should be provided. Parking management in such areas cannot be handled in a one size fits all approach but must be tailored to the specific circumstances of each district. Opportunities for intervention include making better use of available parking on street, forming partnerships with private parking providers to share parking, and varying parking rates on a block by block basis to better distribute demand. Opportunities also arise for moving some parkers out of cars and into other modes such as transit, biking, walking. Managing parking thus entails not only the technical work of parking inventories, occupancy surveys, and price setting, but also a broader set of demand management strategies. In addition, the political work of managing interests and expectations is part of the process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".