Planning a high-frequency transfer-based bus network: How do we get there?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As cities have grown more dispersed and auto-oriented, demand for travel has become increasingly difficult to meet via public transit. Public transit ridership, particularly bus ridership, has recently been on the decline in many urban areas in Canada and the United States, and many agencies have either undergone or are planning comprehensive bus network redesigns in response. While comprehensive bus network redesigns are not novel to public transit, network redesigns are commonly being considered in cities to optimize operational costs and reverse downward trends in transit ridership. For cities considering a comprehensive bus network redesign, there is currently no comprehensive easy-to-follow planning process available to guide cities through such a major undertaking. In light of that, this study presents a methodology to guide transport professionals through the planning process of a bus network redesign, using Longueuil, Quebec, as a case study. Currently, Longueuil operates a door-to-door network, and the goal is to move to a transfer-based, high-frequency service while keeping the existing number of buses constant. A variety of data sources that capture regional travel behavior and network performance are overlaid using a GIS-based grid-cell model to identify priority bus corridors. A series of analyses to measure and quantify anticipated and actual improvements from the proposed bus network redesign are conducted, including coverage analysis, change in accessibility to jobs, and travel time analysis. Accessibility to jobs was the key performance measure used in this analysis and is presented as a useful tool for planners and transit agencies to obtain buy-in for the proposed plan. This methodology provides transport professionals with a flexible and reproducible guide to consider when conducting a bus network redesign, while ensuring that such a network overhaul maximizes the number of opportunities that residents can access by transit and does not add an additional burden to an agency’s operating budget.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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