Impact of a public transit strike on public bicycle share use: An interrupted time series natural experiment study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Promoting active transportation is an important public health objective. Limited research has examined the potential of interventions that highly constrain transportation and their potential impact on cycling. From November 1-7th, 2016, Philadelphia's transit workers went on strike, stopping all transit services in the city. We used the strike event as a natural experiment to examine the impact of public transit strikes on use of Philadelphia's bicycle share program. We estimated the impact of the strike using two separate approaches, interrupted time series and Bayesian structural time series models. We estimated the impact of the intervention overall and stratified by membership type (members and non-members). Models controlled for the weather in Philadelphia (daily temperature and precipitation), and the rate of bicycle share use per 100,000 people in Washington, Boston, and Chicago. We estimate the strike caused an increase of between 86 and 92 trips per 100,000 population (57% increase in use) on average in Philadelphia during the strike period. After the strike ridership quickly returned to baseline, decreasing by 80 trips per 100,000 population after the strike. Similarly, members and non-member ridership increased by 41 and 49 trips per 100,000 population on average during the strike period and quickly returned to baseline, respectively. Our results suggest that interventions that highly constrain transit can increase active transportation but the behavior may not be sustained after transit becomes available again.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it