Defining a Primary Market and Estimating Demand for Major Bicycle-Sharing Program in Philadelphia, Pennsylvania
Bibliographic record
Abstract
Public bicycle-sharing (bike share) programs have become increasingly popular in recent years, particularly in Europe, with a number of cities recently implementing systems and high levels of usage. North American efforts have been more limited to date, with high-profile recent examples including a small program in Washington, D.C., and a substantial seasonal program in Montreal, Canada. Because there are no established large-scale programs in the United States, planners exploring potential system designs and feasibilities are faced with an unusual degree of uncertainty about who would ride, where they might ride, and how often they might ride. A large-scale bike share system is under consideration in Philadelphia, Pennsylvania. This paper discusses the methods and findings of a two-phase project that (a) used a raster-based geographic information system analysis to identify a primary geographic market area for a bike share program and (b) applied bike share trip diversion rates observed in peer European cities to estimate daily bike share trips in the primary market area. This analysis resulted in estimates for daily usage in Philadelphia that ranged from roughly 6,000 to 23,000 for two scales of market area and three demand scenarios (low, middle, and high). As bike share systems continue to proliferate in different settings, new data can refine the methods used here to provide increasing levels of certainty in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".