Assessing the Efficiency of Household Residential Location Choices
Bibliographic record
Abstract
As part of strategic plans, we often see car dependency reduction vision along with strategies to reduce car use and vehicle-kilometers traveled while promoting alternatives such as transit and active modes. It is less common to see strategies to generate more structural changes, even if such change can have much more important and sustainable impacts. Whereas it is well known that home location is one of the key drivers of travel behaviors, it is much less frequent to have planners put forward strategies to encourage people to move and choose their locations more wisely with respect to their needs. This research aims to assess the potential collective gain of an optimal allocation of households to available dwellings. It aims to estimate how inefficient the current distribution is of households among the dwellings with respect to where all household members need to travel. Results show that the household relocations reduce the distances for work and study by 37.9%. This reduction saves an average of 13.8 km per household per day or 4.9 km per work or study trip. If the mode choice remains constant despite the new trip conditions following the household relocations, the total mileage for work and study trips would decrease by 42.8% for car drivers, by 35.2% for car passenger, by 13.3% for school bus, and 34.2% for public transport. As a result of the household relocations, walking and cycling latent trips increased, respectively, from 2.6% to 15.5% and 26.1% to 39.9% of motorized trips.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".