The origin-destination matrix as an indicator of intrahousehold travel allocation
Bibliographic record
Abstract
Subareas throughout a city may be viewed as both daytime destinations for some persons as well as residence (or nighttime) locations for some households. Associated with the average household in each subarea is the distribution of its members by their daytime destinations. Travel allocation of individuals by their principal subarea of daytime destination can be thus constructed for the average household in each subarea throughout a city. Intrahousehold allocation of daytime destinations can thus be represented in a convenient tabular form, the household composition matrix. Further interpretation shows the household composition matrix to be a special case of an origin-destination (O-D) matrix representing commuter volumes between subareas of nighttime and daytime location. Formal features of the household composition matrix, furthermore, render it equivalent to the Leontief input-output matrix. The relationship between intrahousehold travel allocation and household composition, as an O-D matrix, emerges to be of particular importance within the context of Leontief's input-output concept. Application to the Seoul Metropolitan Area indicates a discernable pattern in intrahousehold travel allocation when ordering of the household composition matrix is based on ratios between daytime and nighttime populations across the city's subareas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".