Transit network analysis: providing an optimal transit network strategy for mid-size transit systems
Bibliographic record
Abstract
Transit network analysis is an emerging field in transportation planning. This practicum addresses the issue of declining transit ridership in many North American regions and the trend towards rethinking transit networks to improve ridership and transit modal share. While there is existing research reporting on large cities such as Houston and Seattle, the focus of this practicum is on transit agencies in mid-sized regions having a population serving area between 100,000 and 1,000,000 residents, with two case studies in Columbus and Kansas City. Redesigning a transit network requires transit planners to carefully consider current land use patterns, ridership/coverage ratio, and most importantly the political environment. The process typically will take years to accomplish. Columbus took four years to successfully roll out their redesigned network to positive results, while Kansas City is in its first full year of planning for a network redesign strategy and are encountering numerous obstacles unique to the region. In addition to examining how to redesign transit networks for better efficiency, this practicum identifies other innovative strategies transit planners are considering in improving ridership and modal share, such as microtransit, universal transit passes, and low-income transit passes. While most of the research focuses on transit agencies in Columbus and Kansas City, several elements can be applied to other transit agencies that are considering a redesign of their transit network. A questionnaire was developed that was sent to all North American transit agencies in mid-sized regions, and five planners were interviewed in Columbus and Kansas City to learn more about the process of transit network restructuring. Findings and recommendations include determining the optimal balance between providing ridership and coverage service in the transit network, realizing that transit network restructuring is a long-term process, and remembering there are other tools that can be used to attract riders such as rider incentives and microtransit. Future research opportunities can include a focus on Canadian transit agencies, winter cities, how transit agencies balance providing frequent bus service in major corridors and coverage service elsewhere, and revisiting Kansas City after they complete their transit network restructuring process.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".