Subjectivity matters: Investigating the relationship between perceived accessibility and travel behaviour
Bibliographic record
Abstract
Accessibility, the ease of reaching destinations, encompass local and regional metrics used to evaluate the performance of the land use and transport systems in a region. These measures are known to impact individuals’ travel behaviour, which led to their adoption in practice as performance indicators to evaluate transport projects and plans, monitor progress towards equity goals, and evaluate changes induced by various policies. However, calculated measures of accessibility do not account for individuals’ experiences and perceptions, which play a pivotal role in travel behaviour. This research examines the relationship between travel behaviour and perceived accessibility, while accounting for calculated accessibility, residential selection, travel identity, and individual characteristics. Using data from a large-scale bilingual online survey administered in Montreal, Canada in Fall 2023 (N = 5,277), we perform statistical analyses at both local and regional levels to model weekly mode shares for walking and public transit, respectively. Calculated accessibility is accounted for locally using Walk Score® and regionally using cumulative opportunities accessibility measures by public transit. Our findings reveal that perceived accessibility by walking and public transit positively impact the weekly walking and transit mode share, respectively, for all purposes. Accounting for calculated accessibility and travel identity is important to avoid overestimating the influence of perceived accessibility on travel behaviour. This research provides transport professionals a nuanced understanding of the link between accessibility (perceived and calculated) and travel behaviour, offering insights for promoting the use of sustainable travel modes.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".