Alternative Bus Stop Configuration: An Analysis of the Effects of Bus Bulbs
Bibliographic record
Abstract
Bus bulbs are sections of sidewalk that extend from the curb of a parking lane to the edge of the through lane. A major advantage of using bus bulbs is the creation of additional space at a bus stop for shelters, benches, and other bus patron improvements when the inclusion of these amenities would otherwise be limited without the additional space. Several large cities on the West Coast have begun to explore bus bulbs as one of manhy strategies used in developing a transit preferential program. Researchers visited four transit agencies that use bus bulbs (San Francisco, Portland, Seattle, and Vancouver, British Columbia) to observe and document existing and planned bus bulbs. Before and after studies were conducted to determine if there was a change in pedestrian and traffic operations after the installation of bus bulbs. The bus bulb design was clearly an improvement in pedestrian space as compared to the bus bay design. The average amount of available spae for pedestrians and transit patrons alike improved from 19 to 44 square feet/pedestrian (1.8 to 4.1 sq m/ped) after the bulb was constructed. The replacement of a bus bay with a bus bulb improved cehicle and bus speeds on the block. The block with the farside stop saw a statistically significant increase in vehicle travel speed during both nonpeak (9.5 to 15.7 mph [15.3 to 25.3 km/h]) and peak (11.4 to 20.9 [18.4 to 33.6 km/h]) periods.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".