Exploring sustainable accessibility through multimodal networks: Assessing healthy food access in Montreal
Bibliographic record
Abstract
• High-resolution accessibility modelling reveals persistent food access gaps at short time thresholds in Montreal. • Cycling greatly reduces disparities in access to healthy food relative to walking-only scenarios. • Combining active travel with transit maximizes access to healthy food. • Land use and transportation integration are crucial to addressing spatial disparities in food access. Equitable access to essential amenities is crucial for sustainable urban areas, yet achieving it remains challenging in North American cities characterized by car-centric development and sprawling urban patterns. This paper investigates how multimodal transportation networks reshape access to “Healthy Food Establishments (HFEs)” in Montreal. Using high-resolution block-level data, verified HFE locations, and multimodal routing with the r5r engine, we model three accessibility scenarios: walking-only, cycling-only, and combined active travel plus transit. Accessibility is measured through a cumulative-opportunity and gravity-based metric, capturing temporal thresholds and the variety of destinations. Our analysis reveals significant disparities in food accessibility under walking-only conditions, with nearly half of Montreal residents lacking adequate access within a strict 10-minute timeframe. Cycling considerably improves accessibility, yet the highest levels of equitable access are only achieved through integrated multimodal scenarios that combine active transportation and public transit. Our results underscore the importance of planning multimodal transit nodes that facilitate integrated trip-chaining, emphasizing the need for policy interventions that prioritize continuous, low-stress cycling infrastructure, high-frequency transit services, and clustered essential amenities to bridge accessibility gaps in underserved areas.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".