Evolving transport mode changes: A longitudinal analysis of built-environment exposure in Montréal, Canada
Bibliographic record
Abstract
Understanding the impacts of exposure to local and regional accessibility on travel behavior is essential to develop long-term effective land-use and transport policies. Previous research concentrating on accessibility impacts were mostly of cross-sectional nature and were conducted using pre-pandemic data. This study examines the longitudinal relationships between exposure to different levels of local and regional accessibility and mode use, focusing on how home relocation affects the frequency of use of the three major transport modes: active transport, driving, and public transit. The study uses five waves (2019–2024) of the Montréal Mobility Survey, to analyze 4550 panel respondents, split into worker ( N = 3067) and non-worker ( N = 1483) subsamples. Using a set of multilevel linear regressions and a cumulative exposure measure, this work analyzes the gradual impacts of home relocation and changes in exposure levels to regional and local accessibility on weekly mode use frequency over time while controlling for car ownership and household structure. The study provides robust longitudinal evidence on how residential relocation, built-environment exposure, and concurrent life decisions collectively reshape urban travel behavior in the post-pandemic era across different transport modes. The multilevel modeling approach reveals three key insights: (1) regional and local accessibility changes (through relocation) exert gradual and mode-specific effects, with active transport showing the strongest response; (2) while workers and non-workers show varying baseline travel patterns, both groups respond similarly to local and regional accessibility improvements and changes in car ownership; and (3) car ownership decisions can significantly moderate the effects of home relocation. These findings advance the methodological integration of longitudinal exposure measures to levels of accessibility in mobility research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".