Cycling for Everyone: Incorporating Equity Principles into Transportation Planning Process
Bibliographic record
Abstract
Transportation, especially bicycling, has become a key focus for cities to provide equitable access to amenities and improve environmental conditions. Notably, cycling offers an accessible mode of transportation, crucial for disadvantaged communities. However, disparities exist where affluent areas often receive better cycling infrastructure, leaving behind those who could benefit most. Using Montreal as a case study, this research aims to address transportation equity through three main questions: the differences in travel behaviors among socio-demographic groups, the variation in access to cycling infrastructure, and strategies to prioritize infrastructure for disadvantaged populations.Through an analysis of travel surveys, the research highlights distinct travel patterns among low-income groups, children, and women, with a dependence on public transit and non-motorized transportation. The expansion of Montreal’s bicycle network shows socio-spatial disparities, where moderately wealthy areas enjoy better connectivity. Although disadvantaged areas have access to more destinations, they often lack dedicated cycling infrastructure.The dissertation proposes a quantitative framework for urban planners to identify and prioritize gaps in the cycling network, considering connectivity, equity, and a modal shift towards cycling. It presents a systematic approach to integrate transportation equity into policy, deepening the understanding of travel behaviors across different socio-demographic groups, developing a tool to assess the equity impact of cycling infrastructure allocation, and offering a method to systematically prioritize cycling infrastructure investments for maximum benefit
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".