Analysis of the effect of multi-level urban form on bikeshare demand: Evidence from seven large metropolitan areas in the United States
Bibliographic record
Abstract
Bikeshare programs in their current form have been in place for several years in many cities across the United States. Encouraging people to use bikeshare for their daily routine travel has numerous social, economic, environmental, and health benefits. Therefore, it is important to understand factors influencing bikeshare use in different urban areas to improve the system and encourage more use. This paper investigates how the built environment at both local and regional scales influences bikeshare use in seven large metropolitan areas in the U.S. The study areas include Boston, Chicago, Philadelphia, Minneapolis, San Francisco, San Jose, and Washington, D.C., and the data consists of about 12 million bike trips from approximately 2,000 stations over a one-year period. In addition to linear regression models built for each individual city for comparison purposes, a multi-level mixed effect regression model is built to predict the number of trips originated from each station with respect to the local and regional built environment pattern. The results are consistent with previous research on the effect of land use at the local level on bikeshare demand and show that residential density, regional diversity, pedestrian-oriented road network density, and job accessibility via transit all have a significant positive effect on bikeshare demand. At the regional level, results suggest that the overall level of mixed-use development and overall bike-friendliness in the region (i.e., exclusive bike routes, right-of-way, and bike facilities) and higher congestion level in the region are significant factors influencing bikeshare activities and demand. Models developed in this study could be applied to other communities that are seeking to improve and/or expand their bikeshare systems, as well as cities planning to launch new bikeshare programs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".