Bibliographic record
Abstract
Cycling has become an increasingly popular mode of transportation, offering significant benefits for urban mobility, public health, economic growth, and sustainability. However, urban infrastructure in North America has traditionally been car-centric. Safety and comfort concerns remain significant barriers to broader cycling adoption. As cities strive toward a net-zero future, redesigning urban spaces to encourage a shift toward this more sustainable transportation mode is essential. In line with this vision, this thesis develops quantitative methods for the efficient assessment, planning, and utilization of urban bike infrastructure, with a particular focus on fostering cycling adoption in Toronto, Canada. First, we introduce a computer vision approach to assess cycling stress---the discomfort cyclists experience on urban road networks. This method leverages the widespread availability of street-view images to replace current data- and labour-intensive assessment practices. Next, we formulate an optimization model to determine the optimal locations for new bike lanes, aiming to maximize the city's low-stress cycling accessibility---a metric strongly correlated with cycling mode choice in Toronto. Given the size and complexity of Toronto's road network, this optimization model cannot be solved by any existing methods. In response, we develop a machine learning-augmented optimization approach that is computationally efficient and produces provably high-quality solutions. From 2020--2024, this approach achieves comparable performance to Toronto's implemented plan while reducing the required length of bike lanes by 25%, equivalent to a cost saving of over 18 million Canadian dollars. Alternatively, the same infrastructure investment could have yielded an additional 11.2% increase in cycling accessibility. Finally, we investigate cycling path recommendations in the context of last-mile delivery. Empirical evidence indicates that cycling couriers often deviate from delivery routes prescribed by platforms, leading to increased delivery times and inefficiencies in order batching and assignment, both of which depend heavily on accurate delivery route modelling. To address this, we propose a data-driven approach to prescribing delivery paths that are both high-quality and perceived as such by couriers. The former property ensures service quality, while the latter promotes adoption. In a case study, our pipeline significantly improves route adherence without compromising delivery times compared to current industry practices.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".