The cars are going to be alright: Examining micromobility infrastructure space allocation and potential improvement scenarios in Montréal
Bibliographic record
Abstract
Many cities today are redesigning their streetscapes to redress the historical privilege afforded to the automobile in planning and policy. Much streetscape redesign is around transport infrastructure space, which largely prioritizes car travel and marginalizes other travel modes. Attempts by planners and policy makers to this end often are met with public opposition by advocates of the car, protesting about losing space on the street. This is empirically investigated with the case of Montréal by determining the allocation of street space to transport infrastructures, deriving measures of infrastructure space per traveller, and devising an Equal Infrastructure Allocation score to measure the imbalance between infrastructure provision per travel mode. Per borough, the distribution of transport infrastructure is examined, alongside correlations with demographic, socio-economic, land use, and crash rate variables. Potential scenarios of significant micromobility infrastructure improvement are modelled to test how infrastructure space apportionment per mode changes. This investigation discovers that even large improvements to micromobility infrastructure have a minor effect on space allocated to automobiles. Equal Infrastructure Allocation score and associated indicators are presented as useful tools for planners and policy makers implementing micromobility infrastructure projects, to better communicate with the public and address potential opposition.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".