Role of Spatial Configuration in Commuting Trade-Offs of Two-Worker Households: A Case Study in the Greater Toronto Area
Bibliographic record
Abstract
This research investigates the commuting trade-offs between individuals in two-worker households with home and work locations in the Greater Toronto Area (GTA). A commuting trade-off occurs when a home relocation results in one worker incurring a higher commute distance for the other worker to work closer to the home location. This research uses stated preference data and multilevel modeling to demonstrate that two-worker households adjust their home–work spatial configuration which results in commuting trade-offs between individuals. This research uses the angle between the two workplaces, measured at the home location, as a variable in its empirical model. This variable is a descriptor of the home–work spatial configuration and a predictor of total household commute distance. The modeling results indicate an inverse relationship between total household commute distance and the difference between individual commutes. This suggests that individuals in two-worker households trade off their individual commute distances and, in that process, reduce total household commute distance. A key policy implication arising from this research relates to the jobs–housing balance within a catchment area. Two-worker households have been regarded as a hindrance to achieving jobs–housing balance as the two work locations present a constraint in relocating the home near both work locations. However, as this research shows, workers will trade off their individual commute distances such that a home relocation results in a shorter commute distance for one worker and longer commute distance for the other.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".