Spatial impacts of on-demand transit service for transit stop and neighborhood ridership
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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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".