Activity Space Geometry and Its Effect on Mode Choice
Bibliographic record
Abstract
Understanding and quantifying the effect of built environment variables on travel demand is a topic for which there exists a large body of work. A relatively new sub-field in the urban planning and travel behaviour literature however is the analysis and interpretation of activity spaces. Researchers have associated large activity spaces with environmentally detrimental outcomes as a result of its intuitive correlation with large distances travelled by automobile, but no rigorous attempt has yet been made to define what types of activity space, be they large in area, concentrated along a corridor or otherwise, lead to higher use of either active modes or transit. Using data from origin-destination surveys in Montreal and two for Quebec City, Canada, the following article explores the relationship between area and compactness in activity spaces and describes a new measure for predicting likelihood of transit use. While the effort remains exploratory, the measure is validated using logistic regression. Results indicate that small and compact activity spaces increase the likelihood of active mode use (walking and cycling), that large and compact spaces lead to high personal vehicle use, and that a statistically significant relationship exists between the ratio of area to compactness (dubbed ACR) and transit use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".