Planning a high-frequency transfer-based bus network: How do we get there?
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.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".