Spatial impacts of on-demand transit service for transit stop and neighborhood ridership
Why this work is in the frame
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Bibliographic record
Abstract
• Develops a generalized framework leveraging standard city data sources for the impacts of on-demand transit service. • Characterizes the accessibility and connectivity of on-demand transit service between transit stops and neighborhoods. • Analyzes the collective effects of individual stop-level and neighborhood-level characteristics on ridership. • Develops a multilevel model considering lower and higher hierarchical attributes for on-demand transit ridership analyses. On-demand transit service is being rapidly adopted by many transit agencies due to its advantages on improved mobility. As an emerging service, its implications for ridership and neighborhood improvement of a city are crucial for long-term development. To this end, this paper proposes a framework leveraging standard city data sources to analyze and evaluate the spatial impacts of on-demand transit services for ridership in transit stops and neighborhoods. The utilization of standard data sources enables the framework to be accessible and applicable to vast cities operating on-demand services. A case study of analyses is conducted with real-world data including trips, city census and land use factors at the City of Regina, Canada. We investigated the impacts of on-demand transit accessibility and connectivity, land use and socioeconomic factors on transit stops and neighborhoods. Results indicate that the accessibility and connectivity of the on-demand transit stops positively impact the ridership. It is also found that demand for on-demand transit service is higher for neighborhoods with lower population and income. This indicates the necessity of on-demand transit services for the disadvantaged population. The proposed methods and data sources in this study can serve as a transferable framework for vast on-demand service evaluations. Moreover, the findings of this study also highlight the positive impact of on-demand transit services in shaping transit ridership and neighborhood public transportation.
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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 | 0.000 | 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 it