Road Management Systems to Support Bicycling: A Case Study of Montreal’s Bike Network
Bibliographic record
Abstract
Bicycling is a sustainable mode of transportation given its health benefits, reduced air and noise pollution, savings in fuel consumption, and role in shifting demand away from the automobile. A significant increase of bicycle users is an aim of many cities around the world. Responding to this, various cities announced their strategies to extend and/or upgrade their bikeway networks. However, there is a disconnection between the strategies to support bicycles and road management systems, which are typically used for optimal scheduling of maintenance and interventions for roads’ infrastructure. Traditional road management systems consider neither the need to sustain bicycle pathways at good levels of service, nor consider bicycling demand to prioritize their selection. This thesis extends road management systems to support bicycling networks. This enables the ability to optimally allocate available resources for sustaining the surface of bicycle pathways in good condition, and implement physically-separated bicycle lanes to enhance safety conditions and encourage bicycle ridership. A simple formulation of bicycle demand is proposed; it employs the capabilities of smartphones for collecting and estimating bicycling demand based on GPS trajectories of cyclists. Goal programming optimization is applied to address scheduling of maintenance and upgrade investments of pathways. Two scenarios are investigated with different annual budgets. The results show that the first scenario allows a rapid upgrade of existing bicycle lanes to protected paths while accomplishing good conditions of pavements. However, the second scenario is not able to prevent the deterioration of pavement segments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".