Evaluating bicycle accessibility and bike-bus integration infrastructure : Saskatoon, SK, 2006
Bibliographic record
Abstract
The study proposes an application of Talen’s (2003) methodological framework for assessing neighbourhood-level (i.e., non-motorised mode) accessibility, and offers recommendations for improving non-motorized transportation (NMT) accessibility to enhance multimodal integration between bicycles and buses in contemporary North American suburban neighbourhoods. Accessibility (or "access”) is defined as the average travel time or distance between a given origin and destination along the shortest available street network route. The study considers characteristics of the transportation network such as available route directness, facilities, and transit service provision to determine their impacts on bicycle access. A further methodology for comparing bicycle versus bus modal efficiencies within suburban contexts is developed and applied to the case study. A review of approaches designed to promote bicycling while discouraging personal automobile use provides a toolbox of proven treatments that are applied to a case study of Saskatoon, Saskatchewan – a city of approximately 200,000 people. The approach provides a process that can be used by city or transit planners to identify neighbourhoods that lack sufficient access and apply treatments that improve bicycle accessibility and bicycle-transit integration. Results suggest existing potential for the bicycle as an access mode within contemporary suburban neighbourhood transportation networks. The case study supports the notion that suburban bicycle-bus integration could be used as a viable alternative to automobiles for daily home-to-node activity trips, and raises questions about the current allocation of public transit service to suburban routes within the context of the case study. Discussion and conclusions suggest directions for future research in this field of sustainable urban transportation planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".