Are we connected? Assessing bicycle network performance through directness and connectivity measures, a Montreal, Canada case study
Bibliographic record
Abstract
Over the last two decades, cycling has seen a rise in popularity in North American cities, which are continuously expanding their bicycle networks. While studies highlight that a good network should provide direct bicycle routes for cyclists to reach their desired destination, most network assessments simply measure the length of bicycle facilities in a region. Building on a set of complementary indicators to account for directness of bicycle facilities, this study assesses the performance of the bicycle network in Montreal, Canada. The study uses data from two large-scale online cyclist surveys (2,917 and 2,644 respondents) conducted in Montreal in 2009 and 2013. The 2009 survey provides data on actual trips made by cyclists and is used to assess cyclists’ behavior in Montreal. The 2013 survey provides actual cyclists’ home and work/school locations. Routes between actual home and work locations are generated using the bicycle facilities and street network based on three different route preferences. For each route generated, the diversion and proportion of route on bicycle facilities are calculated to assess directness of bicycle facilities. Based on these two indicators and on Montreal cyclists’ behaviour, network connectivity is then measured. The Montreal network shows a low level of connectivity of less than 51% on every level of preference. Trade-offs between diversion and proportion of route on bicycle facilities are highlighted spatially together with areas with low levels of connectivities. Finally, using circuity measures (the ratio between network and Euclidian distances), results show the extent to which the existing transportation network favors driving with a circuity of 1.22 compared to 1.33 for trips made by bicycle. Using a simple set of performance measures reflecting cyclists’ trade-offs, this study highlights the need to incorporate bicycle network connectivity objectives into transportation plans to improve the efficiency of the network and hence promote bicycle use. Cities wishing to promote cycling can use the set of metrics developed in this study to evaluate their current or projected bicycle network.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".