Analysis of BRT systems and its implications on physical activity using a hurdle model statistical approach
Bibliographic record
Abstract
Past research has explored the relationship between physical activity and public transit availability. Evidence suggests an increase in physical activity linked to public transit; however, research connecting physical activity to changes in transportation is scarce. Many studies focus on BRT systems, which are popular among emerging economies. Using data from Rio de Janeiro’s TransCarioca and Mexico City’s Metrobus, we investigate the effects of a new BRT service on the physical activity of catchment area residents. One problem related to traffic project evaluation research is that interviewed subjects in pre- and post-periods are different; hence, some research groups have applied propensity score matching techniques to reduce bias and achieve systematic concordance between treatment and control groups. Another limitation of this methodology is that it does not consider zero-inflated responses, thus lacking accuracy while estimating the average treatment effect. We present a novel approach where we conduct a matching process using the population’s sociodemographic variables —e.g. gender, age, and marital status— to build the after-mentioned groups, and then we use a hurdle statistical model in which the two processes generating the zeros and the positives are not constrained to be the same. Using the responses to over 8000 IPAQ questionnaires applied in Rio de Janeiro in 2011 and 2015, and in Mexico City in 2011 and 2014, after the implementation of BRT systems, we analyzed the time residents in the catchment area spent walking for utilitarian and recreational purposes using a Cragg double-hurdle regression model. Preliminary results show a statistically significant effect of Mexico City’s Metrobus system implementation on physical activity. At the same time, Rio de Janeiro experienced an increase in the same area after the TransCarioca system implementation, although the transport-mode change is statistically insignificant.
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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 | 0.044 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".