Agency specific insights into bus ridership determinants using a Bayesian framework
Bibliographic record
Abstract
Abstract Understanding the factors that affect bus ridership, particularly at the agency level, is essential for improving urban mobility and sustainability. This study addresses a significant gap in transit literature by proposing a Bayesian method to identify agency-specific factors influencing bus ridership. Using data from the National Transit Database (NTD) of United States from 2007 to 2017, we examine bus ridership across 45 transit agencies in the United States. Our research goes beyond existing studies by exploring agency-specific effects, allowing us to compare how variables such as population, gas prices, and subsidies impact ridership across different systems. We employ fixed-effects and random-effects models, along with a Bayesian framework, to capture variations and identify differences among agencies. This Bayesian approach allows for the incorporation of prior knowledge and uncertainty, enhancing the robustness of our results. By utilizing a select group of relevant variables, our method empowers transit agencies to discern which factors most profoundly influence their ridership patterns, facilitating data-driven decision-making tailored to their specific contexts. Generalizing trends among transit agencies can be challenging due to the significant variations in bus ridership and other influencing factors. This research addresses this issue by identifying the unique challenges faced by individual transit agencies and offering tailored solutions to meet their specific needs.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".