Impact of a public transit strike on public bicycle share use: An interrupted time series natural experiment study
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.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".